{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T18:53:52Z","timestamp":1743101632126,"version":"3.40.3"},"publisher-location":"Cham","reference-count":57,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031563959"},{"type":"electronic","value":"9783031563966"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-56396-6_23","type":"book-chapter","created":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T10:15:14Z","timestamp":1713348914000},"page":"361-379","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning Techniques for the Management of Diseases: A Paper Review"],"prefix":"10.1007","author":[{"given":"Ngolah Kenneth","family":"Tim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vivient","family":"Kamla","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elie T.","family":"Fute","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,18]]},"reference":[{"key":"23_CR1","doi-asserted-by":"crossref","unstructured":"Chui, K.T.: Disease diagnosis in smart healthcare: innovation, technologies and applications. Sustain. Health J. (2017)","DOI":"10.3390\/su9122309"},{"key":"23_CR2","doi-asserted-by":"crossref","unstructured":"Ardabili, S.F., et al.: COVID-19 outbreak prediction with machine learning. MDPI (2020)","DOI":"10.32942\/OSF.IO\/XQ8RB"},{"key":"23_CR3","doi-asserted-by":"crossref","unstructured":"Khanday, A.M.U.D., et al.: Machine learning based approaches for detecting COVID-19 using clinical text data. (2020)","DOI":"10.1007\/s41870-020-00495-9"},{"key":"23_CR4","doi-asserted-by":"crossref","unstructured":"Khader, K.: Machine learning systems in epidemics: in the AI of the storm. Int. J. Comput. Appl. (2020)","DOI":"10.5120\/ijca2020920323"},{"key":"23_CR5","doi-asserted-by":"crossref","unstructured":"Laudanski, K., et al.: What Can COVID-19 teach us about using AI in pandemics? MDPI (2020)","DOI":"10.3390\/healthcare8040527"},{"key":"23_CR6","doi-asserted-by":"crossref","unstructured":"Syeda, H.B., et al.: The role of machine learning techniques to tackle COVID-19 crisis: a systematic review. medRxiv (2020)","DOI":"10.1101\/2020.08.23.20180158"},{"key":"23_CR7","doi-asserted-by":"crossref","unstructured":"Almagooshi, S.: Simulation modelling in healthcare: challenges and trends. Sci. Dir. (2015)","DOI":"10.1016\/j.promfg.2015.07.155"},{"key":"23_CR8","unstructured":"Shinde, S.A., Rajeswari, P.R.: Intelligent health risk prediction systems using machine learning. Int. J. Eng. Technol. (2018)"},{"key":"23_CR9","unstructured":"Matthes, E.: Python Crash Course, A Hands-On, Project-Based Introduction to Programming. 2nd Edition (2023)"},{"key":"23_CR10","doi-asserted-by":"crossref","unstructured":"Cho, G., et al.: Review of machine learning algorithms for diagnosing mental illness. US National library of medicines (2019)","DOI":"10.30773\/pi.2018.12.21.2"},{"key":"23_CR11","unstructured":"Ahsan, et al.: Machine learning based disease diagnosis. Eng., Biomed. Technol. (2021)"},{"key":"23_CR12","unstructured":"El Houby, E.M.F.: A survey on applying machine learning techniques for management of diseases (2017)"},{"key":"23_CR13","doi-asserted-by":"crossref","unstructured":"Dutta, P., et al.: Comparative analysis of various supervised machine learning techniques for diagnosis of COVID-19. ScienceDirect (2021)","DOI":"10.1016\/B978-0-323-85172-5.00020-4"},{"key":"23_CR14","doi-asserted-by":"crossref","unstructured":"Merihi, Y., et al.: Machine learning-based research for COVID-19 detection, diagnosis, and prediction: a survey (2022)","DOI":"10.1007\/s42979-022-01184-z"},{"key":"23_CR15","doi-asserted-by":"crossref","unstructured":"Syeda, H.B.: Role of machine learning techniques to tackle the COVID-19 crisis: systematic review. NIH (2021)","DOI":"10.1101\/2020.08.23.20180158"},{"key":"23_CR16","doi-asserted-by":"crossref","unstructured":"Saleem, F., et al.: Machine learning, deep learning, and mathematical models to analyze forecasting and epidemiology of COVID-19: a systematic literature review. Int. J. Environ. Res. Publ. Health (2022)","DOI":"10.3390\/ijerph19095099"},{"key":"23_CR17","doi-asserted-by":"crossref","unstructured":"Broadbent, A., et al.: Can robots do epidemiology? Machine learning, causal inference, and predicting the outcomes of public health interventions (2022)","DOI":"10.1007\/s13347-022-00509-3"},{"key":"23_CR18","doi-asserted-by":"crossref","unstructured":"Yost, J., et al.: Tools to support evidence-informed public health decision making. BMC Publ. Health (2014)","DOI":"10.1186\/1471-2458-14-728"},{"key":"23_CR19","doi-asserted-by":"crossref","unstructured":"Rose, S.: Intersections of machine learning and epidemiological methods for health services research. Int. J. Epidemiol. (2020)","DOI":"10.1093\/ije\/dyaa035"},{"key":"23_CR20","doi-asserted-by":"crossref","unstructured":"Ibrahim, I., Abdulazeez, A.: The role of machine learning algorithms for diagnosing diseases. JASTT (2021)","DOI":"10.38094\/jastt20179"},{"key":"23_CR21","doi-asserted-by":"crossref","unstructured":"Fatima, M., Pasha, M.: Survey of machine learning algorithms for disease diagnostic. Sci. Res. Publ. (2017)","DOI":"10.4236\/jilsa.2017.91001"},{"key":"23_CR22","unstructured":"Vijayarani, S., Dhayanand, S.: Liver disease prediction using SVM and Na\u00efve Bayes algorithms. Int. J. Sci., Eng. Technol. Res. (IJSETR) (2015)"},{"key":"23_CR23","doi-asserted-by":"crossref","unstructured":"Iyer, A., Jeyalatha, S., Sumbaly, R.: Diagnosis of diabetes using classification mining techniques. Int. J. Data Min. Knowl. Manage. Process (2015)","DOI":"10.5121\/ijdkp.2015.5101"},{"key":"23_CR24","unstructured":"Ramalingam, V.V. et al.: Heart disease prediction using machine learning techniques: a survey. IJET (2018)"},{"key":"23_CR25","doi-asserted-by":"crossref","unstructured":"Pouriyeh, S., et al.: A comprehensive investigation and comparison of machine learning techniques in the domain of heart disease. In: 22nd IEEE Symposium on Computers and Communication (ISCC 2017): Workshops - ICTS4eHealth (2017)","DOI":"10.1109\/ISCC.2017.8024530"},{"key":"23_CR26","doi-asserted-by":"crossref","unstructured":"Alanazi, R.: Identification and prediction of chronic diseases using machine learning approach. J. Healthc. Eng. (2022)","DOI":"10.1155\/2022\/2826127"},{"key":"23_CR27","unstructured":"Wejdan, L., et al.: Diabetic retinopathy detection through deep learning techniques: a review (2020)"},{"key":"23_CR28","unstructured":"Yasseen, A., et al.: Automatic extraction of knowledge for diagnosing COVID-19 disease based on text mining techniques: a systematic review. Periodicals Eng. Nat. Sci. (2021)"},{"key":"23_CR29","unstructured":"Sriram, et al.: Intelligent parkinson disease prediction using machine learning algorithms. IJEIT 3 (2013)"},{"key":"23_CR30","doi-asserted-by":"crossref","unstructured":"Charleonnan, et al.: Predictive analytics for chronic kidney disease using machine learning techniques. MITiCON (2016)","DOI":"10.1109\/MITICON.2016.8025242"},{"key":"23_CR31","doi-asserted-by":"crossref","unstructured":"Kandhasamy, J.P., Balamurali, S.J.P.C.S.: Performance analysis of classifier models to predict diabetes mellitus 47 (2015)","DOI":"10.1016\/j.procs.2015.03.182"},{"key":"23_CR32","doi-asserted-by":"crossref","unstructured":"Shouman, et al.: Applying k-nearest neighbour in diagnosing heart disease patients. ICKD (2012)","DOI":"10.7763\/IJIET.2012.V2.114"},{"key":"23_CR33","doi-asserted-by":"crossref","unstructured":"Lubaib, P., Muneer, K.A.: The heart defect analysis based on PCG signals using pattern recognition techniques. In: ICETEST, vol. 24, pp. 1024\u20131031 (2016)","DOI":"10.1016\/j.protcy.2016.05.225"},{"key":"23_CR34","doi-asserted-by":"crossref","unstructured":"Elmasri, K., et al.: Automatic detection and quantification of abdominal aortic calcification in dual energy X-ray absorptiometry 96 (2016)","DOI":"10.1016\/j.procs.2016.08.116"},{"issue":"4","key":"23_CR35","first-page":"664","volume":"2","author":"B Femina","year":"2015","unstructured":"Femina, B., Anto, S.: Disease diagnosis using rough set-based feature selection and K-nearest neighbor classifier. Int. J. Multi. Res. Dev. 2(4), 664\u2013668 (2015)","journal-title":"Int. J. Multi. Res. Dev."},{"key":"23_CR36","doi-asserted-by":"crossref","unstructured":"Gardezi, S.J.S., et al.: Mammogram classification using deep learning features. In: IEEE, International Conference (2017)","DOI":"10.1109\/ICSIPA.2017.8120660"},{"key":"23_CR37","doi-asserted-by":"crossref","unstructured":"Sayed, A.M., et al.: Automatic classification of breast tumors using features extracted from magnetic resonance images 95 (2016)","DOI":"10.1016\/j.procs.2016.09.350"},{"key":"23_CR38","doi-asserted-by":"crossref","unstructured":"Neelaveni, J., Devasana, M.G.: Alzheimer disease prediction using machine learning algorithms. In: 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS) (2020)","DOI":"10.1109\/ICACCS48705.2020.9074248"},{"key":"23_CR39","unstructured":"Tarmizi, et al.: Classification of dengue outbreak detection using data mining models. JNIT 4, 96\u2013107"},{"key":"23_CR40","unstructured":"Seyedamin, P., et al.: A comprehensive investigation and comparison of machine learning techniques in the domain of heart disease. In: IEEE, International Conference (2017)"},{"key":"23_CR41","unstructured":"Hussain, A., et al.: Heart disease diagnosis using the brute force algorithm and machine learning techniques. In: CMC 2022, vol. 76, no. 2"},{"key":"23_CR42","doi-asserted-by":"crossref","unstructured":"Acharya, U.R., et al.: Application of higher-order spectra for the characterization of Coronary artery disease using electrocardiogram signals 31 (2017)","DOI":"10.1016\/j.bspc.2016.07.003"},{"key":"23_CR43","doi-asserted-by":"crossref","unstructured":"Tayefi, M., et al.: hs-CRP is strongly associated with coronary heart disease (CHD): a data mining approach using decision tree algorithm 141 (2017)","DOI":"10.1016\/j.cmpb.2017.02.001"},{"key":"23_CR44","doi-asserted-by":"crossref","unstructured":"El Houby, E.M.: A framework for prediction of response to HCV therapy using different data mining techniques. NIH (2014)","DOI":"10.1155\/2014\/181056"},{"key":"23_CR45","doi-asserted-by":"crossref","unstructured":"Guo, J., et al.: Revealing determinant factors for early breast cancer recurrence by decision tree (2017)","DOI":"10.1007\/s10796-017-9764-0"},{"key":"23_CR46","doi-asserted-by":"crossref","unstructured":"Khalilabad, N.D., et al.: Fully automatic classification of breast cancer microarray images. J. Electr. Syst. Inf. Technol. (2016)","DOI":"10.1016\/j.jesit.2016.06.001"},{"key":"23_CR47","unstructured":"Vidushi, A.R., Shrivastava, A.K.: Diagnosis of Alzheimer disease using machine learning approaches. Int. J. Adv. Sci. Technol. 29 (2020)"},{"key":"23_CR48","doi-asserted-by":"crossref","unstructured":"Thomas, M., et al.: Automatic ECG arrhythmia classification using dual tree complex wavelet based features 59 (2015)","DOI":"10.1016\/j.aeue.2014.12.013"},{"key":"23_CR49","doi-asserted-by":"crossref","unstructured":"Kaya, Y., Uyar, M.: A hybrid decision support system based on rough set and extreme learning machine for diagnosis of hepatitis disease 13 (2013)","DOI":"10.1016\/j.asoc.2013.03.008"},{"key":"23_CR50","doi-asserted-by":"crossref","unstructured":"Jilani, T.A., et al.: PCA-ANN for classification of Hepatitis-C patients. Int. J. Comput. Appl. 14 (2011)","DOI":"10.5120\/1899-2530"},{"key":"23_CR51","doi-asserted-by":"crossref","unstructured":"Resino, S., et al.: An artificial neural network improves the non-invasive diagnosis of significant fibrosis in HIV\/HCV coinfected patients 62 (2011)","DOI":"10.1016\/j.jinf.2010.11.003"},{"key":"23_CR52","unstructured":"Amin,R.,: Machine learning algorithms for depression: diagnosis, insights, and research directions. MDPI (2022)"},{"key":"23_CR53","unstructured":"Zhang, C., et al.: Prediction of shield tunneling-induced ground settlement using machine learning\u201d techniques (2019)"},{"key":"23_CR54","unstructured":"Korolev, A., et al.: 3D DenseNet ensemble in 4-way classification of Alzheimer\u2019s disease (2020)"},{"key":"23_CR55","doi-asserted-by":"crossref","unstructured":"Chen, L., et al.: Synthetic data in machine learning for medicine and healthcare. Nat. Biomed. Eng. (2021)","DOI":"10.1038\/s41551-021-00751-8"},{"key":"23_CR56","doi-asserted-by":"crossref","unstructured":"Shah, S., et al.: Diagnosis of COVID-19 using CT scan images and deep learning techniques (2021)","DOI":"10.1007\/s10140-020-01886-y"},{"key":"23_CR57","unstructured":"Fatima, M., Pasha, M.: Comparative analysis of meta learning algorithms for liver disease detection (2017)"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Safe, Secure, Ethical, Responsible Technologies and Emerging Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-56396-6_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,17]],"date-time":"2024-04-17T10:18:19Z","timestamp":1713349099000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-56396-6_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031563959","9783031563966"],"references-count":57,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-56396-6_23","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"18 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SAFER-TEA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Safe, Secure, Ethical, Responsible Technologies and Emerging Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Yaound\u00e9","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cameroon","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"safertea2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/safertea.eai-conferences.org\/2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Confy +","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"75","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}