{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T04:41:03Z","timestamp":1773204063384,"version":"3.50.1"},"reference-count":175,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T00:00:00Z","timestamp":1687132800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T00:00:00Z","timestamp":1687132800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"UTM Zamalah Scholarship"},{"DOI":"10.13039\/501100003093","name":"Ministry of Higher Education, Malaysia","doi-asserted-by":"publisher","award":["Reference No: FRGS\/1\/2020\/STG05\/UTM\/02\/17 (UTM Vote No: R.J130000.7851.5F394)."],"award-info":[{"award-number":["Reference No: FRGS\/1\/2020\/STG05\/UTM\/02\/17 (UTM Vote No: R.J130000.7851.5F394)."]}],"id":[{"id":"10.13039\/501100003093","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s10462-023-10528-x","type":"journal-article","created":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T21:11:56Z","timestamp":1687209116000},"page":"15449-15494","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A review on the significance of body temperature interpretation for early infectious disease diagnosis"],"prefix":"10.1007","volume":"56","author":[{"given":"Nurul Izzati Darul","family":"Zaman","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4929-3508","authenticated-orcid":false,"given":"Yuan Wen","family":"Hau","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming Chern","family":"Leong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3625-9869","authenticated-orcid":false,"given":"Rania Hussien Ahmed","family":"Al-ashwal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,19]]},"reference":[{"key":"10528_CR1","first-page":"4102","volume":"12","author":"SA Alasadi","year":"2017","unstructured":"Alasadi SA, Bhaya WS (2017) Review of data preprocessing techniques in data mining. J Eng Appl Sci 12:4102\u20134107","journal-title":"J Eng Appl Sci"},{"key":"10528_CR200","doi-asserted-by":"publisher","unstructured":"Alvarado LI, Lorenzi OD, TorresVela\u00b4squez BC, Sharp TM, Vargas L, Mu\u00f1oz-Jorda\u00b4n JL, et al. (2019) Distinguishing patients with laboratory-confirmed chikungunya from dengue and other acute febrile illnesses, Puerto Rico, 2012\u20132015. PLoS Negl Trop Dis 13(7):e0007562. https:\/\/doi.org\/10.1371\/journal.pntd.0007562","DOI":"10.1371\/journal.pntd.0007562"},{"key":"10528_CR2","doi-asserted-by":"publisher","first-page":"662","DOI":"10.4103\/0019-5049.19062","volume":"60","author":"Z Ali","year":"2016","unstructured":"Ali Z, Bhaskar SB (2016) Basic statistical tools in research and data analysis. Indian J Anaesth 60:662\u2013669. https:\/\/doi.org\/10.4103\/0019-5049.19062","journal-title":"Indian J Anaesth"},{"key":"10528_CR3","doi-asserted-by":"publisher","DOI":"10.1109\/SCORED.2015.7449347","author":"M Ali","year":"2015","unstructured":"Ali M, Zain JM, Zolkipli MF, Badshah G (2015) Battery efficiency of mobile devices through computational offloading: A review. 2015 IEEE Student Conference on Research and Development, SCOReD. https:\/\/doi.org\/10.1109\/SCORED.2015.7449347","journal-title":"2015 IEEE Student Conference on Research and Development, SCOReD."},{"key":"10528_CR4","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.amj.2009.04.013","volume":"28","author":"S Allua","year":"2009","unstructured":"Allua S, Thompson CB (2009) Inferential statistics. Air Med J 28:168\u2013171. https:\/\/doi.org\/10.1016\/j.amj.2009.04.013","journal-title":"Air Med J"},{"key":"10528_CR5","first-page":"1","volume":"5","author":"BM Alsafy","year":"2019","unstructured":"Alsafy BM, Aydam ZM, Mutlag WK (2019) Multiclass classification methods: a review. Int J Adv Eng Technol Innov Sci 5:1\u201310","journal-title":"Int J Adv Eng Technol Innov Sci"},{"key":"10528_CR6","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1016\/j.jpainsymman.2013.02.012","volume":"46","author":"NJ Ames","year":"2013","unstructured":"Ames NJ, Peng C, Powers JH, Leidy NK, Miller-Davis C, Rosenberg A, Vanraden M, Wallen GR (2013) Beyond intuition: patient fever symptom experience. J Pain Symptom Manage 46:807\u2013816. https:\/\/doi.org\/10.1016\/j.jpainsymman.2013.02.012","journal-title":"J Pain Symptom Manage"},{"key":"10528_CR7","doi-asserted-by":"publisher","DOI":"10.1186\/s12955-017-0644-6","author":"NJ Ames","year":"2017","unstructured":"Ames NJ, Powers JH, Ranucci A, Gartrell K, Yang L, VanRaden M, Leidy NK, Wallen GR (2017) A systematic approach for studying the signs and symptoms of fever in adult patients: the fever assessment tool (FAST). Health Quality Life Outcomes. https:\/\/doi.org\/10.1186\/s12955-017-0644-6","journal-title":"Health Quality Life Outcomes"},{"key":"10528_CR8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1348\/000711004849268","volume":"57","author":"A Andres Martin","year":"2004","unstructured":"Andres Martin A, Femia Marzo P (2004) Delta: a new measure of agreement between two raters. Br J Math Stat Psychol 57:1\u201319","journal-title":"Br J Math Stat Psychol"},{"key":"10528_CR9","doi-asserted-by":"publisher","DOI":"10.1051\/matecconf\/201815006003","author":"S Anwar Lashari","year":"2018","unstructured":"Anwar Lashari S, Ibrahim R, Senan N, Taujuddin NSAM (2018) Application of data mining techniques for medical data classification: a review. MATEC Web Conf. https:\/\/doi.org\/10.1051\/matecconf\/201815006003","journal-title":"MATEC Web Conf"},{"key":"10528_CR10","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1002\/mpr.329","volume":"20","author":"MJ Azur","year":"2011","unstructured":"Azur MJ, Stuart EA, Frangakis C, Leaf PJ (2011) Multiple imputation by chained equations: what is it and how does it work? Int J Methods Psychiatr Res 20:40\u201349","journal-title":"Int J Methods Psychiatr Res"},{"key":"10528_CR11","doi-asserted-by":"publisher","DOI":"10.1088\/1755-1315\/20\/1\/012038","author":"NIS Bahari","year":"2014","unstructured":"Bahari NIS, Ahmad A, Aboobaider BM (2014) Application of sup-port vector machine for classification of multispectral data. IOP Conf Ser. https:\/\/doi.org\/10.1088\/1755-1315\/20\/1\/012038","journal-title":"IOP Conf Ser"},{"key":"10528_CR12","first-page":"1811","volume":"13","author":"R Bala","year":"2017","unstructured":"Bala R, Kumar D (2017) Classification using ANN: a review. Int J Comput Intell Res 13:1811\u20131820","journal-title":"Int J Comput Intell Res"},{"key":"10528_CR13","doi-asserted-by":"publisher","first-page":"3","DOI":"10.2307\/3315487","volume":"27","author":"M Banerjee","year":"1999","unstructured":"Banerjee M, Capozzoli M, McSweeney L, Sinha D (1999) Beyond kappa: a review of interrater agreement measures. Can J Stat 27:3\u201323. https:\/\/doi.org\/10.2307\/3315487","journal-title":"Can J Stat"},{"key":"10528_CR14","first-page":"491","volume":"26","author":"IL Bennett","year":"1954","unstructured":"Bennett IL (1954) The significance of fever in infections. Yale J Biol Med 26:491\u2013505","journal-title":"Yale J Biol Med"},{"key":"10528_CR15","doi-asserted-by":"publisher","first-page":"1520","DOI":"10.1111\/jocn.12343","volume":"23","author":"J Bettany-Saltikov","year":"2014","unstructured":"Bettany-Saltikov J, Whittaker VJ (2014) Selecting the most appropriate inferential statistical test for your quantitative research study. J Clin Nurs 23:1520\u20131531. https:\/\/doi.org\/10.1111\/jocn.12343","journal-title":"J Clin Nurs"},{"key":"10528_CR16","first-page":"252","volume":"5","author":"S Bhojani","year":"2016","unstructured":"Bhojani S, Bhatt N (2016) Data mining techniques and trends-a review. Glob J Res Anal 5:252\u2013254","journal-title":"Glob J Res Anal"},{"key":"10528_CR17","unstructured":"Biau G (2012) Analysis of a random forests model. J Mach Learn Res 13:1063\u20131095"},{"key":"10528_CR18","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1016\/j.ijid.2020.11.177","volume":"103","author":"YP Bria","year":"2021","unstructured":"Bria YP, Yeh CH, Bedingfield S (2021) Significant symptoms and nonsymptom-related factors for malaria diagnosis in endemic regions of Indonesia. Int J Infect Dis 103:194\u2013200. https:\/\/doi.org\/10.1016\/j.ijid.2020.11.177","journal-title":"Int J Infect Dis"},{"key":"10528_CR19","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-017-0340-6","author":"M Carpentier","year":"2017","unstructured":"Carpentier M, Combescure C, Merlini L, Tv P (2017) Kappa statistic to measure agreement beyond chance in free-response assessments. BMC Med Res Methodol. https:\/\/doi.org\/10.1186\/s12874-017-0340-6","journal-title":"BMC Med Res Methodol"},{"key":"10528_CR20","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0254088","author":"A Ceney","year":"2021","unstructured":"Ceney A, Tolond S, Glowinski A, Marks B, Swift S, Palser T (2021) Accuracy of online symptom checkers and the potential impact on service utilisation. PLoS ONE. https:\/\/doi.org\/10.1371\/journal.pone.0254088","journal-title":"PLoS ONE"},{"issue":"2","key":"10528_CR21","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1007\/978-3-642-25789-6_80","volume":"125","author":"X Chen","year":"2012","unstructured":"Chen X, Lei Y (2012) Effects of sample size on accuracy and stability of species distribution models: a comparison of GARP and Maxent. Lect Notes Electr Eng 125(2):601\u2013609. https:\/\/doi.org\/10.1007\/978-3-642-25789-6_80","journal-title":"Lect Notes Electr Eng"},{"key":"10528_CR22","doi-asserted-by":"publisher","DOI":"10.1167\/tvst.9.2.14","author":"RY Choi","year":"2020","unstructured":"Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Peter Campbell J (2020) Introduction to machine learning, neural networks, and deep learning. Transl vis Sci Technol. https:\/\/doi.org\/10.1167\/tvst.9.2.14","journal-title":"Transl vis Sci Technol"},{"key":"10528_CR23","doi-asserted-by":"publisher","DOI":"10.1136\/fmch-2019-000262","author":"MZI Chowdhury","year":"2020","unstructured":"Chowdhury MZI, Turin TC (2020) Variable selection strategies and its importance in clinical prediction modelling. Family Med Commun Health. https:\/\/doi.org\/10.1136\/fmch-2019-000262","journal-title":"Family Med Commun Health"},{"key":"10528_CR24","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1136\/eb-2015-102092","volume":"19","author":"WL Chua","year":"2016","unstructured":"Chua WL, Liaw SY (2016) Assessing beyond vital signs to detect early patient deterioration. Evid Based Nurs 19:53. https:\/\/doi.org\/10.1136\/eb-2015-102092","journal-title":"Evid Based Nurs"},{"key":"10528_CR25","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1177\/001316446002000104","volume":"20","author":"J Cohen","year":"1960","unstructured":"Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Measur 20:37\u201346. https:\/\/doi.org\/10.1177\/001316446002000104","journal-title":"Educ Psychol Measur"},{"key":"10528_CR26","unstructured":"Cornish R (2006) Statistics: an introduction to sample size calculations. Mathematic Learn Support Centre"},{"key":"10528_CR27","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"TM Cover","year":"1967","unstructured":"Cover TM, Hart PE (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13:21\u201327","journal-title":"IEEE Trans Inf Theory"},{"key":"10528_CR28","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1007\/s11517-007-0200-3","volume":"45","author":"D Cuesta","year":"2007","unstructured":"Cuesta D, Varela M, Mir\u00f3 P, Gald\u00f3s P, Ab\u00e1solo D, Hornero R, Aboy M (2007) Predicting survival in critical patients by use of body temperature regularity measurement based on approximate entropy. Med Biol Eng Comput 45:671\u2013678. https:\/\/doi.org\/10.1007\/s11517-007-0200-3","journal-title":"Med Biol Eng Comput"},{"key":"10528_CR29","doi-asserted-by":"publisher","DOI":"10.3390\/e20110853","author":"D Cuesta-Frau","year":"2018","unstructured":"Cuesta-Frau D, Mir\u00f3-Mart\u00ednez P, Oltra-Crespo S, Jord\u00e1n-N\u00fa\u00f1ez J, Vargas B, Gonz\u00e1lez P, Varela-Entrecanales M (2018) Model selection for body temperature signal classification using both amplitude and ordinality-based entropy measures. Entropy. https:\/\/doi.org\/10.3390\/e20110853","journal-title":"Entropy"},{"key":"10528_CR30","doi-asserted-by":"publisher","first-page":"235","DOI":"10.3934\/mbe.2020013","volume":"17","author":"D Cuesta-Frau","year":"2019","unstructured":"Cuesta-Frau D, Mir\u00f3-Mart\u00ednez P, Oltra-Crespo S, Molina-Pic\u00f3 A, Dakappa PH, Mahabala C, Vargas B, Gonz\u00e1lez P (2019) Classification of fever patterns using a single extracted entropy feature: a feasibility study based on sample entropy. Math Biosci Eng 17:235\u2013249. https:\/\/doi.org\/10.3934\/mbe.2020013","journal-title":"Math Biosci Eng"},{"key":"10528_CR31","doi-asserted-by":"publisher","DOI":"10.3390\/e22091034","author":"D Cuesta-Frau","year":"2020","unstructured":"Cuesta-Frau D, Dakappa PH, Mahabala C, Gupta AR (2020) Fever time series analysis using slope entropy. Application to early unobtrusive differential diagnosis. Entropy. https:\/\/doi.org\/10.3390\/e22091034","journal-title":"Entropy"},{"key":"10528_CR32","doi-asserted-by":"publisher","unstructured":"Cunha BA (1996) The clinical significance of fever patterns. Infect Dis Clin North Am 10:33\u201344. https:\/\/doi.org\/10.1016\/s0891-5520(05)70284-1","DOI":"10.1016\/s0891-5520(05)70284-1"},{"key":"10528_CR33","doi-asserted-by":"publisher","DOI":"10.1101\/2020.04.22.20076141v1","author":"M\u00c9 Czeisler","year":"2020","unstructured":"Czeisler M\u00c9, Marynak K, Clarke KEN, Salah Z, Shakya I, Thierry JM, Ali N, Mcmillan H, Wiley JF, Weaver MD, Czeisler CA, Shantha EE, Rajaratnam MW, Howard ME (2020) Delay or avoidance of medical care because of COVID-19\u2013related concerns\u2014United States, June 2020. US Dep Health Human Serv Cent Dis Control Prev. https:\/\/doi.org\/10.1101\/2020.04.22.20076141v1","journal-title":"US Dep Health Human Serv Cent Dis Control Prev"},{"key":"10528_CR34","doi-asserted-by":"crossref","unstructured":"Dahiwade D, Patle G, Meshram E (2019) Designing Disease Prediction Model Using Machine Learning Approach. Proceedings of the Third international conference on computing methodologies and communication (ICCMC 2019), 1211\u20131215.","DOI":"10.1109\/ICCMC.2019.8819782"},{"key":"10528_CR35","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.5851966","author":"PH Dakappa","year":"2022","unstructured":"Dakappa PH, Chakrapani M (2022) Twenty four hour continuous temperature recordings in patients presented with Undifferentiated fever (Version 1). Zenodo. https:\/\/doi.org\/10.5281\/zenodo.5851966"},{"key":"10528_CR36","doi-asserted-by":"publisher","unstructured":"Dakappa PH, Bhat GK, Bolumbu G, Rao SB, Adappa S, Mahabala C (2016) Comparison of Conventional Mercury Thermometer and Continuous TherCom\u00ae Temperature Recording in Hospitalized Patients. J Clin Diagn Res. 10:OC43-OC46. https:\/\/doi.org\/10.7860\/JCDR\/2016\/21617.8586","DOI":"10.7860\/JCDR\/2016\/21617.8586"},{"key":"10528_CR37","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1615\/CritRevBiomedEng.2018025917","volume":"46","author":"PH Dakappa","year":"2018","unstructured":"Dakappa PH, Prasad K, Rao SB, Bolumbu G, Bhat GK, Mahabala C (2018) Classification of infectious and noninfectious diseases using artificial neural networks from 24-hour continuous tympanic temperature data of patients with undifferentiated fever. Crit Rev Biomed Eng 46:173\u2013183","journal-title":"Crit Rev Biomed Eng"},{"key":"10528_CR38","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1177\/0049475519829600","volume":"49","author":"PH Dakappa","year":"2019","unstructured":"Dakappa PH, Rao SB, Ganaraja B, Bhat GK, Mahabala C (2019) Unique temperature patterns in 24-h continuous tympanic temperature in tuberculosis. Trop Doct 49:75\u201379. https:\/\/doi.org\/10.1177\/0049475519829600","journal-title":"Trop Doct"},{"key":"10528_CR39","first-page":"944","volume-title":"Clinical methods: the history, physical, and laboratory examinations","author":"L Dall","year":"1990","unstructured":"Dall L, Stanford JF (1990) Fever, chills, and night sweats. Clinical methods: the history, physical, and laboratory examinations. Butterworth, Boston, pp 944\u2013948"},{"key":"10528_CR40","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0217-0","author":"S Dash","year":"2019","unstructured":"Dash S, Shakyawar SK, Sharma M, Kaushik S (2019) Big data in healthcare: management, analysis and future prospects. J Big Data. https:\/\/doi.org\/10.1186\/s40537-019-0217-0","journal-title":"J Big Data"},{"key":"10528_CR41","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2334-13-77","author":"RP Daumas","year":"2013","unstructured":"Daumas RP, Passos SRL, Oliveira RVC, Nogueira RMR, Georg I, Marzochi KBF, Brasil P (2013) Clinical and laboratory features that discriminate dengue from other febrile illnesses: a diagnostic accuracy study in Rio de Janeiro, Brazil. BMC Infect Dis. https:\/\/doi.org\/10.1186\/1471-2334-13-77","journal-title":"BMC Infect Dis"},{"key":"10528_CR42","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1177\/0013164418823249","volume":"79","author":"A De Raadt","year":"2019","unstructured":"De Raadt A, Warrens MJ, Bosker RJ, Kiers HAL (2019) Kappa coefficients for missing data. Educ Psychol Measur 79:558\u2013576. https:\/\/doi.org\/10.1177\/0013164418823249","journal-title":"Educ Psychol Measur"},{"key":"10528_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2021.100485","author":"F Desai","year":"2021","unstructured":"Desai F, Chowdhury D, Kaur R, Peeters M, Arya RC, Wander GS, Gill SS, Buyya R (2021) HealthCloud: a system for monitoring health status of heart patients using machine learning and cloud computing. Internet of Things. https:\/\/doi.org\/10.1016\/j.iot.2021.100485","journal-title":"Internet of Things"},{"key":"10528_CR44","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1177\/2192568220911648","volume":"10","author":"JR Dettori","year":"2020","unstructured":"Dettori JR, Norvell DC (2020) Kappa and beyond: is there agreement? Glob Spine J 10:499\u2013501. https:\/\/doi.org\/10.1177\/2192568220911648","journal-title":"Glob Spine J"},{"key":"10528_CR45","first-page":"3544","volume":"3","author":"R Dharmarajan","year":"2015","unstructured":"Dharmarajan R, Vijayasanthi R (2015) An overview on data preprocessing methods in data mining. Int J Sci Res Dev 3:3544\u20133546","journal-title":"Int J Sci Res Dev"},{"key":"10528_CR46","doi-asserted-by":"publisher","DOI":"10.3390\/s18082414","author":"D Dias","year":"2018","unstructured":"Dias D, Cunha JPS (2018) Wearable health devices\u2014vital sign monitoring, systems, and technologies. Sensors (switzerland). https:\/\/doi.org\/10.3390\/s18082414","journal-title":"Sensors (switzerland)"},{"key":"10528_CR47","doi-asserted-by":"publisher","DOI":"10.1186\/cc12894","author":"AM Drewry","year":"2013","unstructured":"Drewry AM, Fuller BM, Bailey TC, Hotchkiss RS (2013) Body temperature patterns as a predictor of hospital-acquired sepsis in afebrile adult intensive care unit patients: a case-control study. Crit Care. https:\/\/doi.org\/10.1186\/cc12894","journal-title":"Crit Care"},{"key":"10528_CR48","doi-asserted-by":"publisher","first-page":"502","DOI":"10.1134\/S0005117919030093","volume":"80","author":"YA Dubnov","year":"2019","unstructured":"Dubnov YA (2019) Entropy-based estimation in classification problems. Autom Remote Control 80:502\u2013512. https:\/\/doi.org\/10.1134\/S0005117919030093","journal-title":"Autom Remote Control"},{"key":"10528_CR49","doi-asserted-by":"publisher","DOI":"10.1136\/adc.2007","author":"AS El-Radhi","year":"2019","unstructured":"El-Radhi AS (2019) Fever in common infectious diseases. Clin Man Fever Child. https:\/\/doi.org\/10.1136\/adc.2007","journal-title":"Clin Man Fever Child"},{"key":"10528_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1148\/ryai.2021200126","volume":"3","author":"BJ Erickson","year":"2021","unstructured":"Erickson BJ, Kitamura F (2021) Performance metrics for machine learning models. Radiology 3:1\u20137. https:\/\/doi.org\/10.1148\/ryai.2021200126","journal-title":"Radiology"},{"key":"10528_CR51","doi-asserted-by":"publisher","first-page":"4434","DOI":"10.1016\/j.eswa.2014.01.011","volume":"41","author":"N Esfandiari","year":"2014","unstructured":"Esfandiari N, Babavalian MR, Moghadam AME, Tabar VK (2014) Knowledge discovery in medicine: current issue and future trend. Expert Syst Appl 41:4434\u20134463. https:\/\/doi.org\/10.1016\/j.eswa.2014.01.011","journal-title":"Expert Syst Appl"},{"key":"10528_CR52","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1381\/1\/012014","author":"HR Fajrin","year":"2019","unstructured":"Fajrin HR, Ilahi MR, Handoko BS, Sari IP (2019) Body temperature monitoring based on telemedicine. J Phys Conf Ser. https:\/\/doi.org\/10.1088\/1742-6596\/1381\/1\/012014","journal-title":"J Phys Conf Ser"},{"key":"10528_CR53","doi-asserted-by":"publisher","DOI":"10.1186\/s12879-016-2024","author":"E Fern\u00e1ndez","year":"2016","unstructured":"Fern\u00e1ndez E, Smieja M, Walter SD, Loeb M (2016) A predictive model to differentiate dengue from other febrile illness. BMC Infect Dis. https:\/\/doi.org\/10.1186\/s12879-016-2024","journal-title":"BMC Infect Dis"},{"key":"10528_CR54","first-page":"218","volume-title":"Statistical methods for rates and proportions","author":"JL Fleiss","year":"1981","unstructured":"Fleiss JL (1981) Statistical methods for rates and proportions. Wiley, London, p 218"},{"key":"10528_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-816948-3.00001-5","author":"A Gabor","year":"2019","unstructured":"Gabor A, Popescu M, Popa-Iovanut F, Naaji A (2019) Mobile application for medical diagnosis. Telemed Technol. https:\/\/doi.org\/10.1016\/B978-0-12-816948-3.00001-5","journal-title":"Telemed Technol"},{"key":"10528_CR56","unstructured":"Gaffert P, Meinfelder F, Bosch V (2016) Towards an MI-proper predictive mean matching."},{"key":"10528_CR57","unstructured":"Gallagher NB (2020) Savitzky-Golay smoothing and differentiation filter. Eigenvector Research Incorporated"},{"key":"10528_CR58","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1038\/jp.2011.72","volume":"32","author":"A Garingo","year":"2012","unstructured":"Garingo A, Friedlich P, Tesoriero L, Patil S, Jackson P, Seri I (2012) The use of mobile robotic telemedicine technology in the neonatal intensive care unit. J Perinatol 32:55\u201363. https:\/\/doi.org\/10.1038\/jp.2011.72","journal-title":"J Perinatol"},{"key":"10528_CR59","doi-asserted-by":"publisher","DOI":"10.1093\/ofid\/ofz032","author":"II Geneva","year":"2019","unstructured":"Geneva II, Cuzzo B, Fazili T, Javaid W (2019) Normal body temperature: a systematic review. Open Forum Infect Dis. https:\/\/doi.org\/10.1093\/ofid\/ofz032","journal-title":"Open Forum Infect Dis"},{"key":"10528_CR60","unstructured":"G\u00e9ron A (2019) Hands-on machine learning with scikit-learn, keras, and tensorflow: concepts, tools, and techniques to build intelligent systems, 2nd edn."},{"key":"10528_CR61","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-019-01649-9","author":"KMU Ghori","year":"2020","unstructured":"Ghori KMU, Imran M, Nawaz A, Abbasi RA, Ullah A, Szathmary L (2020) Performance analysis of machine learning classifiers for non-technical loss detection. J Ambient Intell Humaniz Comput. https:\/\/doi.org\/10.1007\/s12652-019-01649-9","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"10528_CR62","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.procs.2016.04.103","volume":"83","author":"J G\u00f3mez","year":"2016","unstructured":"G\u00f3mez J, Oviedo B, Zhuma E (2016) Patient monitoring system based on internet of things. Procedia Comput Sci 83:90\u201397. https:\/\/doi.org\/10.1016\/j.procs.2016.04.103","journal-title":"Procedia Comput Sci"},{"key":"10528_CR64","doi-asserted-by":"publisher","DOI":"10.1109\/INCET49848.2020.9154130","author":"S Grampurohit","year":"2020","unstructured":"Grampurohit S, Sagarnal C (2020) Disease prediction using machine learning algorithms. Int Conf Emerg Technol (INCET). https:\/\/doi.org\/10.1109\/INCET49848.2020.9154130","journal-title":"Int Conf Emerg Technol (INCET)"},{"key":"10528_CR65","doi-asserted-by":"publisher","DOI":"10.1136\/fmch-2018-000067","author":"TC Guetterman","year":"2019","unstructured":"Guetterman TC (2019) Basics of statistics for primary care research. Family Med Community Health. https:\/\/doi.org\/10.1136\/fmch-2018-000067","journal-title":"Family Med Community Health"},{"key":"10528_CR66","first-page":"143","volume":"19","author":"MM Hamad","year":"2014","unstructured":"Hamad MM, Qader BA (2014) Data pre-processing for knowledge discovery. Tikrit J Pure Sci 19:143\u2013148","journal-title":"Tikrit J Pure Sci"},{"key":"10528_CR67","doi-asserted-by":"publisher","DOI":"10.1016\/j.resplu.2021.100116","author":"M Hamza","year":"2021","unstructured":"Hamza M, Alsma J, Kellett J, Brabrand M, Christensen EF, Cooksley T, Haak HR, Nanayakkara PWB, MertenH SB, Weichert I, Subbe CP (2021) Can vital signs recorded in patients\u2019 homes aid decision making in emergency care? A scoping review. Resusc plus. https:\/\/doi.org\/10.1016\/j.resplu.2021.100116","journal-title":"Resusc plus"},{"key":"10528_CR68","volume-title":"Data mining concepts and techniques","author":"J Han","year":"2012","unstructured":"Han J, Kamber M, Pei J (2012) Data mining concepts and techniques, 3rd edn. Elsevier, Amsterdam","edition":"3"},{"key":"10528_CR69","first-page":"1131","volume":"8","author":"M Hanumanthappa","year":"2017","unstructured":"Hanumanthappa M (2017) Data collection methods and data pre-processing techniques for healthcare data using data mining. Int J Sci Eng Res 8:1131\u20131136","journal-title":"Int J Sci Eng Res"},{"key":"10528_CR70","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1183\/20734735.006616","volume":"13","author":"G Hardavella","year":"2017","unstructured":"Hardavella G, Aamli-Gaagnat A, Frille A, Saad N, Niculescu A, Powell P (2017) Top tips to deal with challenging situations: doctor\u2013patient interactions. Breathe 13:129\u2013135. https:\/\/doi.org\/10.1183\/20734735.006616","journal-title":"Breathe"},{"key":"10528_CR71","doi-asserted-by":"publisher","DOI":"10.1186\/s12910-021-00679-3","author":"NB Heyen","year":"2021","unstructured":"Heyen NB, Salloch S (2021) The ethics of machine learning-based clinical decision support: an analysis through the lens of professionalisation theory. BMC Med Ethics. https:\/\/doi.org\/10.1186\/s12910-021-00679-3","journal-title":"BMC Med Ethics"},{"key":"10528_CR72","doi-asserted-by":"publisher","DOI":"10.1186\/s12879-014-0623-z","author":"SY Huang","year":"2014","unstructured":"Huang SY, Lee IK, Wang L, Liu JW, Hung SC, Chen CC, Chang TY, Huang WC (2014) Use of simple clinical and laboratory predictors to differentiate influenza from dengue and other febrile illnesses in the emergency room. BMC Infect Dis. https:\/\/doi.org\/10.1186\/s12879-014-0623-z","journal-title":"BMC Infect Dis"},{"key":"10528_CR73","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.cmpb.2018.05.007","volume":"62","author":"A Idri","year":"2018","unstructured":"Idri A, Benhar H, Fern\u00e1ndez-Alem\u00e1n JL, Kadi I (2018) A systematic map of medical data preprocessing in knowledge discovery. Comput Methods Progr Biomed 62:69\u201385. https:\/\/doi.org\/10.1016\/j.cmpb.2018.05.007","journal-title":"Comput Methods Progr Biomed"},{"key":"10528_CR74","first-page":"568","volume":"15","author":"FO Isinkaye","year":"2017","unstructured":"Isinkaye FO, Awosupin SO, Soyemi J (2017) A mobile based expert system for disease diagnosis and medical advice provisioning. Int J Comput Sci Inform Secur 15:568\u2013572","journal-title":"Int J Comput Sci Inform Secur"},{"key":"10528_CR75","doi-asserted-by":"publisher","DOI":"10.7860\/JCDR\/2018\/35200.11260","author":"V Jain","year":"2018","unstructured":"Jain V, Jyotsana Chopra A, Mir KA, Babu C, Kohli S, Kapur P, Manjavkar S (2018) Clinicobiochemical difference of patients presenting with dengue and chikungunya during post-monsoon season. J Clin Diagn Res. https:\/\/doi.org\/10.7860\/JCDR\/2018\/35200.11260","journal-title":"J Clin Diagn Res"},{"key":"10528_CR76","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.35","author":"AEW Johnson","year":"2016","unstructured":"Johnson AEW, Pollard TJ, Shen L, Lehman LWH, Feng M, Ghassemi M, Moody B, Szolovits P, Anthony Celi L, Mark RG (2016a) MIMIC-III, a freely accessible critical care database. Sci Data. https:\/\/doi.org\/10.1038\/sdata.2016.35","journal-title":"Sci Data"},{"key":"10528_CR77","doi-asserted-by":"publisher","DOI":"10.13026\/C2XW26","author":"A Johnson","year":"2016","unstructured":"Johnson A, Pollard T, Mark R (2016b) MIMIC-III clinical database (version 1.4). PhysioNet. https:\/\/doi.org\/10.13026\/C2XW26"},{"key":"10528_CR78","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.jcrc.2016.09.013","volume":"37","author":"J Jordan","year":"2017","unstructured":"Jordan J, Miro\u2013Martinez P, Vargas B, Varela-Entrecanales M, Cuesta-Frau D, (2017) Statistical models for fever forecasting based on advanced body temperature monitoring. J Crit Care 37:136\u2013140. https:\/\/doi.org\/10.1016\/j.jcrc.2016.09.013","journal-title":"J Crit Care"},{"key":"10528_CR79","doi-asserted-by":"publisher","first-page":"78","DOI":"10.13005\/ojcst13.0203.03","volume":"13","author":"AP Joshi","year":"2020","unstructured":"Joshi AP, Patel BV. (2020) Data preprocessing: the techniques for preparing clean and quality data for data analytics process. Orient J Comput Sci Technol 13:78\u201381. https:\/\/doi.org\/10.13005\/ojcst13.0203.03","journal-title":"Orient J Comput Sci Technol"},{"key":"10528_CR80","doi-asserted-by":"publisher","first-page":"82","DOI":"10.4103\/idoj.IDOJ_468_18","volume":"10","author":"F Kaliyadan","year":"2018","unstructured":"Kaliyadan F, Kulkarni V (2018) Types of variables, descriptive statistics, and sample size. Indian Dermatol Online J 10:82\u201386. https:\/\/doi.org\/10.4103\/idoj.IDOJ_468_18","journal-title":"Indian Dermatol Online J"},{"key":"10528_CR81","doi-asserted-by":"publisher","first-page":"185","DOI":"10.9734\/jpri\/2021\/v33i33b31810","volume":"33","author":"SD Kamble","year":"2021","unstructured":"Kamble SD, Patel P, Fulzele P, Bangde Y, Musale H, Gaddamwar S (2021) Disease diagnosis system using machine learning. J Pharm Res Int 33:185\u2013194. https:\/\/doi.org\/10.9734\/jpri\/2021\/v33i33b31810","journal-title":"J Pharm Res Int"},{"key":"10528_CR82","doi-asserted-by":"publisher","first-page":"402","DOI":"10.4097\/kjae.2013.64.5.402","volume":"64","author":"H Kang","year":"2013","unstructured":"Kang H (2013) The prevention and handling of the missing data. Korean J Anesthesiol 64:402\u2013406. https:\/\/doi.org\/10.4097\/kjae.2013.64.5.402","journal-title":"Korean J Anesthesiol"},{"key":"10528_CR83","first-page":"354","volume":"3","author":"A Kataria","year":"2013","unstructured":"Kataria A, Singh M (2013) A review of data classification using knearest neighbour algorithm. Int J Emerg Technol Adv Eng 3:354\u2013360","journal-title":"Int J Emerg Technol Adv Eng"},{"key":"10528_CR84","first-page":"1460","volume":"8","author":"R Kavitha","year":"2020","unstructured":"Kavitha R, Mohanapriya N, Prabaharan P, Scholar PG (2020) Disease prediction based on symptoms using machine learning algorithm. AEGAEUM J 8:1460\u20131465","journal-title":"AEGAEUM J"},{"key":"10528_CR85","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.3661426","author":"R Keniya","year":"2020","unstructured":"Keniya R, Khakharia A, Shah V, Vrushabh RM, G, Thaker T, Warang M, Mehendale N, (2020) Disease prediction from various symptoms using machine learning. SSRN Electr J. https:\/\/doi.org\/10.2139\/ssrn.3661426","journal-title":"SSRN Electr J"},{"key":"10528_CR86","doi-asserted-by":"crossref","first-page":"108","DOI":"10.18502\/jad.v12i2.36","volume":"12","author":"W Khan","year":"2018","unstructured":"Khan W, Zakai HA, Khan K, Kausar S, Aqeel S (2018) Original article discriminating clinical and biological features in malaria and dengue patients. J Arthropod-Borne Dis 12:108\u2013118","journal-title":"J Arthropod-Borne Dis"},{"key":"10528_CR87","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.hrtlng.2009.06.019","volume":"39","author":"P Kiekkas","year":"2010","unstructured":"Kiekkas P, Velissaris D, Karanikolas M, Aretha D, Samios A, Skartsani C, Baltopoulos GI, Filos KS (2010) Peak body temperature predicts mortality in critically ill patients without cerebral damage. Heart Lung 39:208\u2013216. https:\/\/doi.org\/10.1016\/j.hrtlng.2009.06.019","journal-title":"Heart Lung"},{"key":"10528_CR88","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1111\/j.1749-6632.1998.tb08329.x","volume":"856","author":"MJ Kluger","year":"1998","unstructured":"Kluger MJ, Kozak W, Conn CA, Leon LR, Soszynski D (1998) Role of fever in disease. Ann NY Acad Sci 856:224\u2013233","journal-title":"Ann NY Acad Sci"},{"key":"10528_CR89","doi-asserted-by":"publisher","DOI":"10.1186\/s40249-017-0238-x","author":"M Kotepui","year":"2017","unstructured":"Kotepui M, PhunPhuech B, Phiwklam N, Uthaisar K (2017) Differentiating between dengue fever and malaria using hematological parameters in endemic areas of Thailand. Infect Dis Poverty. https:\/\/doi.org\/10.1186\/s40249-017-0238-x","journal-title":"Infect Dis Poverty"},{"key":"10528_CR90","first-page":"111","volume":"1","author":"S Kotsiantis","year":"2006","unstructured":"Kotsiantis S, Kanellopoulos D, Pintelas PE (2006) Data preprocessing for supervised learning. Int J Comput Sci 1:111\u2013117","journal-title":"Int J Comput Sci"},{"key":"10528_CR91","doi-asserted-by":"publisher","DOI":"10.4018\/978-1-5225-7131-5.ch010","author":"U Kumar","year":"2018","unstructured":"Kumar U (2018) Applications of machine learning in disease pre-screening. Pre-Screen Syst Early Dis Predict Detect Prev. https:\/\/doi.org\/10.4018\/978-1-5225-7131-5.ch010","journal-title":"Pre-Screen Syst Early Dis Predict Detect Prev"},{"key":"10528_CR92","first-page":"159","volume":"33","author":"JR Landis","year":"1977","unstructured":"Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Int Biom Soc 33:159\u2013174","journal-title":"Int Biom Soc"},{"key":"10528_CR93","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2020.00473","author":"JR Larsen","year":"2020","unstructured":"Larsen JR, Martin MR, Martin JD, Kuhn P, Hicks JB (2020) Modeling the onset of symptoms of COVID-19. Front Public Health. https:\/\/doi.org\/10.3389\/fpubh.2020.00473","journal-title":"Front Public Health"},{"key":"10528_CR94","first-page":"422","volume-title":"Statistics, Descriptive","author":"J Lee","year":"2009","unstructured":"Lee J (2009) Statistics, Descriptive. Elsevier, Amsterdam, pp 422\u2013428"},{"key":"10528_CR95","first-page":"356","volume":"39","author":"C Lee Ventola","year":"2014","unstructured":"Lee Ventola C (2014) Mobile devices and apps for health care professionals uses and benefits. Peer-Rev J Formul Man-Agement 39:356\u2013364","journal-title":"Peer-Rev J Formul Man-Agement"},{"key":"10528_CR96","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1007\/s00134-002-1619-5","volume":"29","author":"JY Lefrant","year":"2003","unstructured":"Lefrant JY, Muller L, Emmanuel Coussaye J, Benbabaali M, Lebris C, Zeitoun N, Mari C, Sa\u00efssi G, Ripart J, Eledjam JJ (2003) Temperature measurement in intensive care patients: comparison of urinary bladder, oesophageal, rectal, axillary, and inguinal methods versus pulmonary artery core method. Intensive Care Med 29:414\u2013418. https:\/\/doi.org\/10.1007\/s00134-002-1619-5","journal-title":"Intensive Care Med"},{"key":"10528_CR97","doi-asserted-by":"crossref","first-page":"287","DOI":"10.6000\/1929-6029.2015.04.03.7","volume":"4","author":"Y Liu","year":"2015","unstructured":"Liu Y, De A (2015) Multiple imputation by fully conditional specification for dealing with missing data in a large epidemiologic study. Int J Stat Med Res 4:287\u2013295","journal-title":"Int J Stat Med Res"},{"key":"10528_CR98","doi-asserted-by":"publisher","first-page":"20","DOI":"10.4018\/IJMCMC.2014100102","volume":"6","author":"Y Liu","year":"2014","unstructured":"Liu Y, Zhou Y, Wen S, Tang C (2014) A strategy on selecting performance metrics for classifier evaluation. Int J Mob Comput Multimed Commun 6:20\u201335. https:\/\/doi.org\/10.4018\/IJMCMC.2014100102","journal-title":"Int J Mob Comput Multimed Commun"},{"key":"10528_CR99","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1080\/03091902.2021.1873441","volume":"45","author":"G Machin","year":"2021","unstructured":"Machin G, Brettle D, Fleming S, Nutbrown R, Simpson R, Stevens R, Tooley M (2021) Is current body temperature measurement practice fit-for-purpose? J Med Eng Technol 45:136\u2013144. https:\/\/doi.org\/10.1080\/03091902.2021.1873441","journal-title":"J Med Eng Technol"},{"key":"10528_CR100","first-page":"11528","volume":"29","author":"MY Madhusekhar","year":"2020","unstructured":"Madhusekhar MY, Nanda Kishore Kumar G, Umapavankumar K, Mantru Naik MR (2020) Frequently used classification algorithms in machine learning with comparative analysis of various parameters. Int J Adv Sci Technol 29:11528\u201311529","journal-title":"Int J Adv Sci Technol"},{"key":"10528_CR101","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-09823-4","volume-title":"Data mining and knowledge discovery handbook","author":"O Maimon","year":"2010","unstructured":"Maimon O, Rokach L (2010) Data mining and knowledge discovery handbook, 2nd edn. Springer, Boston","edition":"2"},{"key":"10528_CR102","unstructured":"Manoj K, Senthamarai Kannan K (2013) Comparison of methods for detecting outliers. Int J Sci Eng Res 4:709\u2013714"},{"key":"10528_CR103","doi-asserted-by":"crossref","first-page":"276","DOI":"10.11613\/BM.2012.031","volume":"22","author":"ML McHugh","year":"2012","unstructured":"McHugh ML (2012) Interrater reliability: the kappa statistic. Biochemia Medica 22:276\u2013282","journal-title":"Biochemia Medica"},{"key":"10528_CR104","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1093\/jamia\/ocy026","volume":"25","author":"AND Meyer","year":"2018","unstructured":"Meyer AND, Thompson PJ, Khanna A, Desai S, Mathews BK, Yousef E, Av K, Singh H (2018) Evaluating a mobile application for improving clinical laboratory test ordering and diagnosis. J Am Med Inform Assoc 25:841\u2013847. https:\/\/doi.org\/10.1093\/jamia\/ocy026","journal-title":"J Am Med Inform Assoc"},{"key":"10528_CR105","doi-asserted-by":"publisher","DOI":"10.4103\/2455-5568.196883","author":"SS Mishra","year":"2016","unstructured":"Mishra SS, Nitika, (2016) Understanding the calculation of the kappa statistic: a measure of inter-observer reliability. Int J Acad Med. https:\/\/doi.org\/10.4103\/2455-5568.196883","journal-title":"Int J Acad Med"},{"key":"10528_CR106","doi-asserted-by":"publisher","first-page":"297","DOI":"10.4103\/aca.ACA_248_18","volume":"22","author":"P Mishra","year":"2019","unstructured":"Mishra P, Pandey C, Singh U, Keshri A, Sabaretnam M (2019) Selection of appropriate statistical methods for data analysis. Ann Card Anaesth 22:297\u2013301. https:\/\/doi.org\/10.4103\/aca.ACA_248_18","journal-title":"Ann Card Anaesth"},{"key":"10528_CR107","doi-asserted-by":"publisher","first-page":"3509","DOI":"10.11591\/eei.v11i6.4225","volume":"11","author":"S Mishra","year":"2022","unstructured":"Mishra S, Kumar R, Tiwari SK, Ranjan P (2022) A review: machine learning approaches in the diagnosis of infectious diseases. Bull Electr Eng Inform 11:3509\u20133520. https:\/\/doi.org\/10.11591\/eei.v11i6.4225","journal-title":"Bull Electr Eng Inform"},{"key":"10528_CR108","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1111\/ijn.12329","volume":"21","author":"WQ Mok","year":"2015","unstructured":"Mok WQ, Wang W, Liaw SY (2015) Vital signs monitoring to detect patient deterioration: an integrative literature review. Int J Nurs Pract 21:91\u201398. https:\/\/doi.org\/10.1111\/ijn.12329","journal-title":"Int J Nurs Pract"},{"key":"10528_CR109","doi-asserted-by":"publisher","first-page":"3243","DOI":"10.1001\/archinte.160.21.3243","volume":"160","author":"AS Monto","year":"2000","unstructured":"Monto AS, Gravenstein S, Elliott M, Colopy M, Schweinle J (2000) Clinical signs and symptoms predicting influenza infection. Arch Intern Med 160:3243\u20133247. https:\/\/doi.org\/10.1001\/archinte.160.21.3243","journal-title":"Arch Intern Med"},{"key":"10528_CR110","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/j.eurpsy.2009.02.004","volume":"25","author":"J Mordal","year":"2010","unstructured":"Mordal J, Gundersen \u00d8, Bramness, JG (2010) Norwegian version of the mini-international neuropsychiatric interview: feasibility, acceptability and test-retest reliability in an acute psychiatric ward. Eur Psychiatry 25:172\u2013177. https:\/\/doi.org\/10.1016\/j.eurpsy.2009.02.004","journal-title":"Eur Psychiatry"},{"key":"10528_CR111","doi-asserted-by":"publisher","DOI":"10.2196\/19194","author":"J Moura","year":"2021","unstructured":"Moura J, Almeida AMP, Roque F, Figueiras A, Herdeiro MT (2021) A mobile app to support clinical diagnosis of upper respiratory problems (eHealthResp): co-design approach. J Med Internet Res. https:\/\/doi.org\/10.2196\/19194","journal-title":"J Med Internet Res"},{"key":"10528_CR112","doi-asserted-by":"publisher","first-page":"6996","DOI":"10.35940\/ijrte.C6038.098319","volume":"8","author":"V Mudaliar","year":"2019","unstructured":"Mudaliar V, Savaridaasan P, Garg S (2019) Disease prediction and drug recommendation android application using data mining (Virtual doctor). Int J Recent Technol Eng 8:6996\u20137001. https:\/\/doi.org\/10.35940\/ijrte.C6038.098319","journal-title":"Int J Recent Technol Eng"},{"key":"10528_CR113","unstructured":"Murtianto YH, Sutrisno S (2017) Thinking process of students with high-mathematics ability (a study on QSR NVivo 11-assisted data analysis). Int J Appl Eng Res. https:\/\/www.researchgate.net\/publication\/319872859"},{"key":"10528_CR114","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1001\/archinte.1979.03630480015009","volume":"139","author":"DM Musher","year":"1979","unstructured":"Musher DM, Fainstein V, Young EJ, Pruett TL (1979) Fever patterns their lack of clinical significance. Arch Intern Med 139:1225\u20131227","journal-title":"Arch Intern Med"},{"key":"10528_CR115","first-page":"879","volume":"3","author":"S Muzammil Ali","year":"2014","unstructured":"Muzammil Ali S, Tuteja R (2014) Data mining techniques. Int J Comput Sci Mob Comput 3:879\u2013883","journal-title":"Int J Comput Sci Mob Comput"},{"key":"10528_CR116","doi-asserted-by":"publisher","unstructured":"Nayak R, Jain LC, Ting BKH. (2001) Artificial neural networks in biomedical engineering: a review. 887\u2013892. https:\/\/doi.org\/10.1016\/B978-0-08-043981-5.50132-2","DOI":"10.1016\/B978-0-08-043981-5.50132-2"},{"key":"10528_CR117","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/J.JIPH.2011.05.002","volume":"4","author":"D Ogoina","year":"2011","unstructured":"Ogoina D (2011) Fever, fever patterns and diseases called \u2018fever\u2019\u2014a review. J Infect Public Health 4:108\u2013124. https:\/\/doi.org\/10.1016\/J.JIPH.2011.05.002","journal-title":"J Infect Public Health"},{"key":"10528_CR118","doi-asserted-by":"publisher","first-page":"259","DOI":"10.30534\/ijeter\/2020\/03822020","volume":"8","author":"A Oguntimilehin","year":"2020","unstructured":"Oguntimilehin A (2020) A mobile malaria fever clinical diagnosis system based on non-nested generalized exemplar (NNGE). Int J Emerg Trends Eng Res 8:259\u2013264. https:\/\/doi.org\/10.30534\/ijeter\/2020\/03822020","journal-title":"Int J Emerg Trends Eng Res"},{"key":"10528_CR119","doi-asserted-by":"publisher","first-page":"528","DOI":"10.30534\/ijatcse\/2020\/72912020","volume":"9","author":"A Oguntimilehin","year":"2020","unstructured":"Oguntimilehin A, Olatunji KA, Abiola OB (2020) A computer based intelligent system for managing typhoid fever. Int J Adv Trends Comput Sci Eng 9:528\u2013532. https:\/\/doi.org\/10.30534\/ijatcse\/2020\/72912020","journal-title":"Int J Adv Trends Comput Sci Eng"},{"key":"10528_CR120","first-page":"1254","volume":"62","author":"A Omair","year":"2012","unstructured":"Omair A (2012) Presenting your results-II inferential statistics. J Pak Med Assoc 62:1254\u20131257","journal-title":"J Pak Med Assoc"},{"key":"10528_CR121","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1111\/jdv.16967","volume":"35","author":"R Pangti","year":"2021","unstructured":"Pangti R, Mathur J, Chouhan V, Kumar S, Rajput L, Shah S, Gupta A, Dixit A, Dholakia D, Gupta S, Gupta S, George M, Sharma VK, Gupta S (2021) A machine learning-based, decision support, mobile phone application for diagnosis of common dermatological diseases. J Eur Acad Dermatol Venereol 35:536\u2013545. https:\/\/doi.org\/10.1111\/jdv.16967","journal-title":"J Eur Acad Dermatol Venereol"},{"key":"10528_CR122","doi-asserted-by":"publisher","DOI":"10.1186\/cc11255","author":"VE Papaioannou","year":"2012","unstructured":"Papaioannou VE, Chouvarda IG, Maglaveras NK, Pneumatikos IA (2012) Temperature variability analysis using wavelets and multiscale entropy in patients with systemic inflammatory response syndrome, sepsis, and septic shock. Crit Care. https:\/\/doi.org\/10.1186\/cc11255","journal-title":"Crit Care"},{"key":"10528_CR123","doi-asserted-by":"publisher","first-page":"1449","DOI":"10.1088\/0967-3334\/34\/11\/1449","volume":"34","author":"VE Papaioannou","year":"2013","unstructured":"Papaioannou VE, Chouvarda IG, Maglaveras NK, Baltopoulos GI, Pneumatikos IA (2013) Temperature multiscale entropy analysis: a promising marker for early prediction of mortality in septic patients. Physiol Meas 34:1449\u20131466. https:\/\/doi.org\/10.1088\/0967-3334\/34\/11\/1449","journal-title":"Physiol Meas"},{"key":"10528_CR124","doi-asserted-by":"publisher","first-page":"154","DOI":"10.4040\/jkan.2013.43.2.154","volume":"43","author":"HA Park","year":"2013","unstructured":"Park HA (2013) An introduction to logistic regression: from basic concepts to interpretation with particular attention to nursing domain. J Korean Acad Nurs 43:154\u2013164. https:\/\/doi.org\/10.4040\/jkan.2013.43.2.154","journal-title":"J Korean Acad Nurs"},{"key":"10528_CR125","unstructured":"Patil P (2020) Disease symptom prediction. . Kaggle. https:\/\/www.kaggle.com\/datasets\/itachi9604\/disease-symptom-description-dataset"},{"key":"10528_CR126","doi-asserted-by":"publisher","unstructured":"Pearce G, Wong J, Mirtskhulava L, Al-Majeed S, Bakuria K, Gulua N (2016) Artificial neural network and mobile applications in medical diagnosis. Proceedings\u2014UKSim-AMSS 17th International conference on computer modelling and simulation, UKSim 61\u201364. https:\/\/doi.org\/10.1109\/UKSim.2015.34","DOI":"10.1109\/UKSim.2015.34"},{"key":"10528_CR127","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1145\/1147234.1147247","volume":"8","author":"RK Pearson","year":"2006","unstructured":"Pearson RK (2006) The problem of disguised missing data. ACM SIGKDD Explor Newsl 8:83\u201392. https:\/\/doi.org\/10.1145\/1147234.1147247","journal-title":"ACM SIGKDD Explor Newsl"},{"key":"10528_CR128","doi-asserted-by":"publisher","first-page":"2297","DOI":"10.1073\/pnas.88.6.2297","volume":"88","author":"SM Pincus","year":"1991","unstructured":"Pincus SM (1991) Approximate entropy as a measure of system complexity. Proc Natl Acad Sci USA 88:2297\u20132301. https:\/\/doi.org\/10.1073\/pnas.88.6.2297","journal-title":"Proc Natl Acad Sci USA"},{"key":"10528_CR129","doi-asserted-by":"publisher","first-page":"97","DOI":"10.5582\/irdr.2016.01009","volume":"5","author":"JJG Plaza","year":"2016","unstructured":"Plaza JJG, Hulak N, Zhumadilov Z, Akilzhanova A (2016) Fever as an important resource for infectious diseases research. Intractable Rare Dis Res 5:97\u2013102. https:\/\/doi.org\/10.5582\/irdr.2016.01009","journal-title":"Intractable Rare Dis Res"},{"key":"10528_CR130","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2018.178","author":"TJ Pollard","year":"2018","unstructured":"Pollard TJ, Johnson AEW, Raffa JD, Celi LA, Mark RG, Badawi O (2018) The eICU collaborative research database, a freely available multi-center database for critical care research. Sci Data. https:\/\/doi.org\/10.1038\/sdata.2018.178","journal-title":"Sci Data"},{"key":"10528_CR131","doi-asserted-by":"publisher","unstructured":"Pollard T, Johnson A, Raffa J, Celi LA, Badawi O, Mark R (2019). eICU Collaborative Research Database (version 2.0). PhysioNet. https:\/\/doi.org\/10.13026\/C2WM1R.","DOI":"10.13026\/C2WM1R"},{"key":"10528_CR132","doi-asserted-by":"publisher","first-page":"7","DOI":"10.5120\/ijca2017915495","volume":"175","author":"KSTDC Potdar","year":"2017","unstructured":"Potdar KSTDC (2017) A comparative study of categorical variable encoding techniques for neural network classifiers. Int J Comput Appl 175:7\u20139. https:\/\/doi.org\/10.5120\/ijca2017915495","journal-title":"Int J Comput Appl"},{"key":"10528_CR133","doi-asserted-by":"publisher","first-page":"1328","DOI":"10.1111\/j.1365-3156.2008.02151.x","volume":"13","author":"JA Potts","year":"2008","unstructured":"Potts JA, Rothman AL (2008) Clinical and laboratory features that distinguish dengue from other febrile illnesses in endemic populations. Trop Med Int Health 13:1328\u20131340. https:\/\/doi.org\/10.1111\/j.1365-3156.2008.02151.x","journal-title":"Trop Med Int Health"},{"key":"10528_CR134","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-92962-x","author":"H Pulido","year":"2021","unstructured":"Pulido H, Stanczyk NM, de Moraes CM, Mescher MC (2021) A unique volatile signature distinguishes malaria infection from other conditions that cause similar symptoms. Sci Rep. https:\/\/doi.org\/10.1038\/s41598-021-92962-x","journal-title":"Sci Rep"},{"key":"10528_CR135","doi-asserted-by":"publisher","DOI":"10.3390\/molecules24152811","author":"A R\u00e1cz","year":"2019","unstructured":"R\u00e1cz A, Bajusz D, H\u00e9berger K (2019) Multi-level comparison of machine learning classifiers and their performance metrics. Molecules (basel, Switzerland). https:\/\/doi.org\/10.3390\/molecules24152811","journal-title":"Molecules (basel, Switzerland)"},{"key":"10528_CR136","doi-asserted-by":"publisher","DOI":"10.17485\/ijst\/2016\/v9i45\/102290","author":"B Ragothaman","year":"2016","unstructured":"Ragothaman B, Sarojini B (2016) A multi-objective non-dominated sorted artificial bee colony feature selection algorithm for medical datasets. Indian J Sci Technol. https:\/\/doi.org\/10.17485\/ijst\/2016\/v9i45\/102290","journal-title":"Indian J Sci Technol"},{"key":"10528_CR137","first-page":"301","volume":"1","author":"BM Ramageri","year":"2010","unstructured":"Ramageri BM (2010) Data mining techniques and applications. Indian J Comput Sci Eng 1:301\u2013305","journal-title":"Indian J Comput Sci Eng"},{"key":"10528_CR138","first-page":"5","volume":"2","author":"P Rao Kalyan Rao","year":"2014","unstructured":"Rao Kalyan Rao P, Ahmed Hanash M, Ahmed AL-Aidaros G, (2014) Development of mobile phone medical application software for clinical diagnosis. Int J Innov Sci Mod Eng (IJISME) 2:5\u20138","journal-title":"Int J Innov Sci Mod Eng (IJISME)"},{"key":"10528_CR139","doi-asserted-by":"crossref","first-page":"344","DOI":"10.12968\/bjon.2019.28.6.344","volume":"28","author":"M Robertson","year":"2019","unstructured":"Robertson M, Hill B (2019) Monitoring temperature. Br J Nurs 28:344\u2013347","journal-title":"Br J Nurs"},{"key":"10528_CR140","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/b978-0-12-801505-6.00006-5","volume-title":"Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment","author":"K Roy","year":"2015","unstructured":"Roy K, Kar S, Das RN (2015) Selected statistical methods in QSAR. Understanding the basics of QSAR for applications in pharmaceutical sciences and risk assessment. Academic Press, Cambridge, pp 191\u2013229. https:\/\/doi.org\/10.1016\/b978-0-12-801505-6.00006-5"},{"key":"10528_CR141","doi-asserted-by":"publisher","unstructured":"Sah S (2020) Machine Learning: A Review of Learning Types. https:\/\/doi.org\/10.20944\/preprints202007.0230.v1","DOI":"10.20944\/preprints202007.0230.v1"},{"key":"10528_CR142","doi-asserted-by":"publisher","DOI":"10.3389\/fnagi.2017.00329","author":"A Sarica","year":"2017","unstructured":"Sarica A, Cerasa A, Quattrone A (2017) Random forest algorithm for the classification of neuroimaging data in Alzheimer\u2019s disease: a systematic review. Front Aging Neurosci. https:\/\/doi.org\/10.3389\/fnagi.2017.00329","journal-title":"Front Aging Neurosci"},{"key":"10528_CR143","doi-asserted-by":"publisher","first-page":"9","DOI":"10.5005\/jp-journals-10081-1283","volume":"3","author":"SP Sawant","year":"2021","unstructured":"Sawant SP, Rudraraju S, Amin AS (2021) Predictive model to differentiate dengue fever from other febrile illnesses in children\u2014application of logistic regression analysis. Pediatr Infect Dis 3:9\u201314. https:\/\/doi.org\/10.5005\/jp-journals-10081-1283","journal-title":"Pediatr Infect Dis"},{"key":"10528_CR144","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.h3480","author":"HL Semigran","year":"2015","unstructured":"Semigran HL, Linder JA, Gidengil C, Mehrotra A (2015) Evaluation of symptom checkers for self diagnosis and triage: audit study. BMJ (online). https:\/\/doi.org\/10.1136\/bmj.h3480","journal-title":"BMJ (online)"},{"key":"10528_CR145","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.eswa.2018.07.014","volume":"114","author":"AM Shabut","year":"2018","unstructured":"Shabut AM, Hoque Tania M, Lwin KT, Evans BA, Yusof NA, Abu-Hassan KJ, Hossain MA (2018) An intelligent mobile-enabled expert system for tuberculosis disease diagnosis in real time. Expert Syst Appl 114:65\u201377. https:\/\/doi.org\/10.1016\/j.eswa.2018.07.014","journal-title":"Expert Syst Appl"},{"key":"10528_CR146","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1093\/ptj\/85.3.257","volume":"85","author":"J Sim","year":"2005","unstructured":"Sim J, Wright CC (2005) The kappa statistic in reliability studies: use, interpretation, and sample size requirements. Phys Ther 85:257\u2013268","journal-title":"Phys Ther"},{"key":"10528_CR147","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2009.07103","author":"A Singh","year":"2020","unstructured":"Singh A, Mohammed A, Chinthala L, Kamaleswaran R (2020) Machine learning predicts early onset of fever from continuous physiological data of critically ill patients. arXiv Preprint. https:\/\/doi.org\/10.48550\/arXiv.2009.07103","journal-title":"arXiv Preprint"},{"key":"10528_CR148","first-page":"785","volume":"117","author":"A Sivakumar","year":"2017","unstructured":"Sivakumar A, Gunasundari R (2017) A survey on data preprocessing techniques for bioinformatics and web usage mining. Int J Pure Appl Math 117:785\u2013794","journal-title":"Int J Pure Appl Math"},{"key":"10528_CR150","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-0221-y","author":"RT Sutton","year":"2020","unstructured":"Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI (2020) An overview of clinical decision support systems: benefits, risks, and strategies for success. Npj Digital Med. https:\/\/doi.org\/10.1038\/s41746-020-0221-y","journal-title":"Npj Digital Med"},{"key":"10528_CR151","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pntd.0000196","author":"L Tanner","year":"2008","unstructured":"Tanner L, Schreiber M, Lo JGH, Ong A, Tolfvenstam T, Lai YL, Ng LC, Leo YS, Puong LT, Vasudevan SG, Simmons CP, Hibberd ML, Ooi EE (2008) Decision tree algorithms predict the diagnosis and outcome of dengue fever in the early phase of illness. PLoS Negl Trop Dis. https:\/\/doi.org\/10.1371\/journal.pntd.0000196","journal-title":"PLoS Negl Trop Dis"},{"key":"10528_CR152","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-99027-z","author":"TL Thein","year":"2021","unstructured":"Thein TL, Ang LW, Young BE, Chen MIC, Leo YS, Lye DCB (2021) Differentiating coronavirus disease 2019 (COVID-19) from influenza and dengue. Sci Rep. https:\/\/doi.org\/10.1038\/s41598-021-99027-z","journal-title":"Sci Rep"},{"key":"10528_CR153","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2020.0111135","author":"D Thi Thu Hien","year":"2020","unstructured":"Thi Thu Hien D, Thi Thu Thuy C, Kim Anh T, The Son D, Nguyen Giap C (2020) Optimize the combination of categorical variable encoding and deep learning technique for the problem of prediction of vietnamese student academic performance. Int J Adv Comput Sci Appl. https:\/\/doi.org\/10.14569\/IJACSA.2020.0111135","journal-title":"Int J Adv Comput Sci Appl"},{"key":"10528_CR154","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0224365","author":"A Vabalas","year":"2019","unstructured":"Vabalas A, Gowen E, Poliakoff E, Casson AJ (2019) Machine learning algorithm validation with a limited sample size. PLoS ONE. https:\/\/doi.org\/10.1371\/journal.pone.0224365","journal-title":"PLoS ONE"},{"key":"10528_CR155","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1016\/j.dsx.2020.04.012","volume":"14","author":"R Vaishya","year":"2020","unstructured":"Vaishya R, Javaid M, Khan IH, Haleem A (2020) Artificial intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab Syndr 14:337\u2013339. https:\/\/doi.org\/10.1016\/j.dsx.2020.04.012","journal-title":"Diabetes Metab Syndr"},{"key":"10528_CR156","doi-asserted-by":"publisher","first-page":"0966","DOI":"10.1371\/journal.pmed.0020267","volume":"2","author":"J van den Broeck","year":"2005","unstructured":"van den Broeck J, Cunningham SA, Eeckels R, Herbst K (2005) Data cleaning: detecting, diagnosing, and editing data abnormalities. PLoS Med 2:0966\u20130970. https:\/\/doi.org\/10.1371\/journal.pmed.0020267","journal-title":"PLoS Med"},{"key":"10528_CR157","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.ijmedinf.2018.12.002","volume":"122","author":"F van Wyk","year":"2019","unstructured":"van Wyk F, Khojandi A, Mohammed A, Begoli E, Davis RL, Kamaleswaran R (2019) A minimal set of physiomarkers in continuous high frequency data streams predict adult sepsis onset earlier. Int J Med Inform 122:55\u201362. https:\/\/doi.org\/10.1016\/j.ijmedinf.2018.12.002","journal-title":"Int J Med Inform"},{"key":"10528_CR158","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1007\/s00421-002-0790-2","volume":"89","author":"M Varela","year":"2003","unstructured":"Varela M, Jimenez L, Fari\u00f1a R (2003) Complexity analysis of the temperature curve: new information from body temperature. Eur J Appl Physiol 89:230\u2013237. https:\/\/doi.org\/10.1007\/s00421-002-0790-2","journal-title":"Eur J Appl Physiol"},{"key":"10528_CR159","doi-asserted-by":"publisher","first-page":"2764","DOI":"10.1097\/01.CCM.0000190157.64486.03","volume":"33","author":"M Varela","year":"2005","unstructured":"Varela M, Calvo M, Chana M, Gomez-Mestre I, Asensio R, Galdos P (2005) Clinical implications of temperature curve complexity in critically ill patients. Crit Care Med 33:2764\u20132771. https:\/\/doi.org\/10.1097\/01.CCM.0000190157.64486.03","journal-title":"Crit Care Med"},{"key":"10528_CR160","doi-asserted-by":"publisher","first-page":"1283","DOI":"10.1111\/j.1742-1241.2011.02794.x","volume":"65","author":"M Varela","year":"2011","unstructured":"Varela M, Ruiz-Esteban R, Martinez-Nicolas A, Cuervo-Arango JA, Barros C, Delgado EG (2011) Catching the spike and tracking the flow: Holter-temperature monitoring in patients admitted in a general internal medicine ward. Int J Clin Pract 65:1283\u20131238. https:\/\/doi.org\/10.1111\/j.1742-1241.2011.02794.x","journal-title":"Int J Clin Pract"},{"key":"10528_CR161","doi-asserted-by":"publisher","first-page":"510","DOI":"10.3390\/e24040510","volume":"24","author":"B Vargas","year":"2022","unstructured":"Vargas B, Cuesta-Frau D, Gonz\u00e1lez-L\u00f3pez P, Fern\u00e1ndez-Cotarelo M-Jos\u00e9, V\u00e1zquez-G\u00f3mez \u00d3, Col\u00e1s A, Varela, M. (2022) Discriminating bacterial infection from other causes of fever using body temperature entropy analysis. Entropy 24:510. https:\/\/doi.org\/10.3390\/e24040510","journal-title":"Entropy"},{"key":"10528_CR162","doi-asserted-by":"publisher","DOI":"10.1063\/5.000774","author":"ST Vasudeva","year":"2020","unstructured":"Vasudeva ST, Rao SS, Panambur NK, Mahabala C, Dakappa PH, Prasad K (2020) Diagnostic classification of undifferentiated fevers using artificial neural network. AIP Conf Proc. https:\/\/doi.org\/10.1063\/5.000774","journal-title":"AIP Conf Proc"},{"key":"10528_CR163","first-page":"86","volume":"14","author":"K Venkatesh","year":"2021","unstructured":"Venkatesh K, Dhyanesh K, Prathyusha M, Naveen Teja CH (2021) Identification of disease prediction based on symptoms using machine learning. J Composition Theory 14:86\u201393","journal-title":"J Composition Theory"},{"key":"10528_CR164","first-page":"360","volume":"37","author":"AJ Viera","year":"2005","unstructured":"Viera AJ, Garrett JM (2005) Understanding interobserver agreement: the kappa statistic. Fam Med 37:360\u2013363","journal-title":"Fam Med"},{"key":"10528_CR165","first-page":"7820","volume":"29","author":"K Vijaya Kumar","year":"2020","unstructured":"Vijaya Kumar K, Raju I, Rudraraju P, Professor A, Professor A (2020) Performance evaluation of machine learning algorithms for disease prediction. Int J Adv Sci Technol 29:7820\u20137830","journal-title":"Int J Adv Sci Technol"},{"key":"10528_CR166","doi-asserted-by":"publisher","DOI":"10.4172\/2161-0487.1000197","author":"MJ Warrens","year":"2015","unstructured":"Warrens MJ (2015) Five ways to look at Cohen\u2019s kappa. J Psychol Psychother. https:\/\/doi.org\/10.4172\/2161-0487.1000197","journal-title":"J Psychol Psychother"},{"key":"10528_CR167","doi-asserted-by":"crossref","first-page":"49","DOI":"10.7748\/ns.12.20.49.s55","volume":"12","author":"R Watson","year":"1996","unstructured":"Watson R (1996) Controlling body temperature in adults. Nurs Stand 12:49\u201355","journal-title":"Nurs Stand"},{"key":"10528_CR168","doi-asserted-by":"publisher","DOI":"10.1145\/3427423.3427426","author":"T Widiyaningtyas","year":"2020","unstructured":"Widiyaningtyas T, Zaeni IAE, Jamilah N (2020) Diagnosis of fever symptoms using naive bayes algorithm. ACM Int Conf Proc Ser. https:\/\/doi.org\/10.1145\/3427423.3427426","journal-title":"ACM Int Conf Proc Ser"},{"key":"10528_CR170","unstructured":"World Health Organization. (2021). World health statistics 2021: monitoring health for the SDGs, sustainable development goals, Geneva"},{"key":"10528_CR171","unstructured":"World Health Organization. (2023a). Dengue and severe dengue. https:\/\/www.who.int\/news-room\/factsheets\/detail\/dengue-and-severe-dengue#"},{"key":"10528_CR169","unstructured":"World Health Organization. (2023b). Tuberculosis. https:\/\/www.who.int\/news-room\/factsheets\/detail\/tuberculosis"},{"key":"10528_CR172","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-021-00467-8","author":"N Yanamala","year":"2021","unstructured":"Yanamala N, Krishna NH, Hathaway QA, Radhakrishnan A, Sunkara S, Patel H, Farjo P, Patel B, Sengupta PP (2021) A vital sign-based prediction algorithm for differentiating COVID-19 versus seasonal influenza in hospitalized patients. Npj Digital Med. https:\/\/doi.org\/10.1038\/s41746-021-00467-8","journal-title":"Npj Digital Med"},{"key":"10528_CR173","doi-asserted-by":"publisher","DOI":"10.2196\/21956","author":"M Zens","year":"2020","unstructured":"Zens M, Brammertz A, Herpich J, S\u00fcdkamp N, Hinterseer M (2020) App-based tracking of self-reported COVID-19 symptoms: analysis of questionnaire data. J Med Internet Res. https:\/\/doi.org\/10.2196\/21956","journal-title":"J Med Internet Res"},{"key":"10528_CR174","doi-asserted-by":"publisher","DOI":"10.1155\/2015\/794586","author":"M Zhu","year":"2015","unstructured":"Zhu M, Xia J, Yan M, Cai G, Yan J, Ning G (2015) Dimensionality reduction in complex medical data: improved self-adaptive niche genetic algorithm. Comput Math Methods Med. https:\/\/doi.org\/10.1155\/2015\/794586","journal-title":"Comput Math Methods Med"},{"key":"10528_CR175","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-00372-6","author":"Y Zoabi","year":"2021","unstructured":"Zoabi Y, Deri-Rozov S, Shomron N (2021) Machine learning-based prediction of COVID-19 diagnosis based on symptoms. Npj Digital Med. https:\/\/doi.org\/10.1038\/s41746-020-00372-6","journal-title":"Npj Digital Med"},{"key":"10528_CR176","doi-asserted-by":"crossref","first-page":"67","DOI":"10.46754\/umtjur.v1i4.93","volume":"1","author":"M Zulhilmi Aqil Muhamad","year":"2019","unstructured":"Zulhilmi Aqil Muhamad M, Hafhizah Abd Rahim N (2019) Expert system to diagnose dengue fever. Univ Malays Terengganu J Undergrad Res 1:67\u201376","journal-title":"Univ Malays Terengganu J Undergrad Res"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-023-10528-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-023-10528-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-023-10528-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,5]],"date-time":"2023-10-05T10:35:51Z","timestamp":1696502151000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-023-10528-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,19]]},"references-count":175,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["10528"],"URL":"https:\/\/doi.org\/10.1007\/s10462-023-10528-x","relation":{"references":[{"id-type":"uri","id":"","asserted-by":"subject"},{"id-type":"uri","id":"","asserted-by":"subject"}]},"ISSN":["0269-2821","1573-7462"],"issn-type":[{"value":"0269-2821","type":"print"},{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,19]]},"assertion":[{"value":"19 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflicts of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}