{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T15:11:54Z","timestamp":1777043514508,"version":"3.51.4"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2020,7,21]],"date-time":"2020-07-21T00:00:00Z","timestamp":1595289600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,7,21]],"date-time":"2020-07-21T00:00:00Z","timestamp":1595289600000},"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":["SN COMPUT. SCI."],"published-print":{"date-parts":[[2020,9]]},"DOI":"10.1007\/s42979-020-00250-8","type":"journal-article","created":{"date-parts":[[2020,7,21]],"date-time":"2020-07-21T16:05:20Z","timestamp":1595347520000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":98,"title":["Predictive Supervised Machine Learning Models for Diabetes Mellitus"],"prefix":"10.1007","volume":"1","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4175-424X","authenticated-orcid":false,"given":"L. J.","family":"Muhammad","sequence":"first","affiliation":[]},{"given":"Ebrahem A.","family":"Algehyne","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7965-8678","authenticated-orcid":false,"given":"Sani Sharif","family":"Usman","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,7,21]]},"reference":[{"key":"250_CR1","unstructured":"Muhammad LJ, Usman SS. Power of artificial intelligence to diagnose and prevent further COVID-19 outbreak: a short communication. 2020. arXiv:2004.12463 [cs.CY]"},{"key":"250_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-020-00216-w","author":"LJ Muhammad","year":"2020","unstructured":"Muhammad LJ, Islam MM, Usman SS, et al. Predictive data mining models for novel coronavirus (COVID-19) infected patients\u2019 recovery. Springer Nat Comput Sci. 2020. https:\/\/doi.org\/10.1007\/s42979-020-00216-w.","journal-title":"Springer Nat Comput Sci."},{"key":"250_CR3","doi-asserted-by":"crossref","unstructured":"Singh P. Supervised machine learning. In: Learn PySpark. Apress, Berkeley. 2019.","DOI":"10.1007\/978-1-4842-4961-1"},{"key":"250_CR4","doi-asserted-by":"crossref","unstructured":"Muhammad LJ, et al. Performance evaluation of classification data mining algorithms on coronary artery disease dataset. In: IEEE 9th international conference on computer and knowledge engineering (ICCKE 2019), Ferdowsi University of Mashhad. 2019.","DOI":"10.1109\/ICCKE48569.2019.8964703"},{"key":"250_CR5","doi-asserted-by":"crossref","unstructured":"Muhammad LJ, et al. Performance evaluation of classification data mining algorithms on coronary artery disease dataset. In: IEEE 9th international conference on computer and knowledge engineering (ICCKE 2019), Ferdowsi University of Mashhad. IEEE. 2019.","DOI":"10.1109\/ICCKE48569.2019.8964703"},{"key":"250_CR6","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.csbj.2016.12.005","volume":"15","author":"I Kavakiotis","year":"2017","unstructured":"Kavakiotis I, et al. Machine learning and data mining methods in diabetes research. Comput Struct Biotechnol J. 2017;15:104\u201316.","journal-title":"Comput Struct Biotechnol J"},{"key":"250_CR7","volume-title":"Machine learning","author":"T Mitchell","year":"1997","unstructured":"Mitchell T. Machine learning. New York: McGraw Hill; 1997."},{"key":"250_CR8","doi-asserted-by":"crossref","unstructured":"Haruna AA, Muhammad LJ, Yahaya BZ, et al. An improved C4.5 data mining driven algorithm for the diagnosis of coronary artery disease. In: International conference on digitization (ICD), Sharjah, United Arab Emirates, 2019. p. 48\u201352.","DOI":"10.1109\/ICD47981.2019.9105844"},{"issue":"3","key":"250_CR9","first-page":"50","volume":"11","author":"LJ Muhammad","year":"2018","unstructured":"Muhammad LJ, Garba EJ, Oye ND, et al. On the problems of knowledge acquisition and representation of expert system for diagnosis of coronary artery disease (CAD). Int J u- and e-Serv Sci Technol. 2018;11(3):50\u20139.","journal-title":"Int J u- and e-Serv Sci Technol"},{"key":"250_CR10","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2997311","author":"F Rustam","year":"2020","unstructured":"Rustam F, et al. COVID-19 future forecasting using supervised machine learning models. IEEE Access. 2020. https:\/\/doi.org\/10.1109\/ACCESS.2020.2997311.","journal-title":"IEEE Access"},{"issue":"11","key":"250_CR11","doi-asserted-by":"publisher","first-page":"197","DOI":"10.14257\/ijdta.2017.10.1.18","volume":"10","author":"LJ Muhammad","year":"2017","unstructured":"Muhammad LJ, et al. Using decision tree data mining algorithm to predict causes of road traffic accidents, its prone locations and time along Kano \u2013Wudil highway. Int J Database Theory Appl. 2017;10(11):197\u2013208.","journal-title":"Int J Database Theory Appl"},{"key":"250_CR12","doi-asserted-by":"publisher","first-page":"64323","DOI":"10.1109\/ACCESS.2019.2917620","volume":"7","author":"Z Gong","year":"2019","unstructured":"Gong Z, Zhong P, Hu W. Diversity in machine learning. IEEE Access. 2019;7:64323\u201350. https:\/\/doi.org\/10.1109\/ACCESS.2019.2917620.","journal-title":"IEEE Access"},{"issue":"1","key":"250_CR13","doi-asserted-by":"publisher","first-page":"14","DOI":"10.4316\/JACSM.201801002","volume":"12","author":"H Sadiq","year":"2018","unstructured":"Sadiq H, Muhammad LJ, Yakubu A. Mining social media and DBpedia data using Gephi and R. J Appl Comput Sci Math. 2018;12(1):14\u201320.","journal-title":"J Appl Comput Sci Math."},{"key":"250_CR14","doi-asserted-by":"crossref","first-page":"39","DOI":"10.33832\/ijast.2020.136.04","volume":"136","author":"FS Ishaq","year":"2020","unstructured":"Ishaq FS, Muhammad LJ, Yahaya BZ, et al. Fuzzy based expert system for diagnosis of diabetes mellitus. Int J Adv Sci Technol. 2020;136:39\u201350.","journal-title":"Int J Adv Sci Technol"},{"key":"250_CR15","doi-asserted-by":"publisher","first-page":"42","DOI":"10.17485\/ijst\/2018\/v11i42\/132665","volume":"11","author":"FS Ishaq","year":"2018","unstructured":"Ishaq FS, Muhammad LJ, Yahaya BZ, et al. Data mining driven models for diagnosis of diabetes mellitus: a survey. Indian J Sci Technol. 2018;11:42.","journal-title":"Indian J Sci Technol"},{"issue":"3","key":"250_CR16","first-page":"166","volume":"50","author":"MA Garcia","year":"2001","unstructured":"Garcia MA. ESDIABETES (an expert system in diabetes). Eur J Sci Res. 2001;50(3):166\u201375.","journal-title":"Eur J Sci Res"},{"issue":"36","key":"250_CR17","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1542\/peds.105.3.671","volume":"105","author":"American Diabetes Association","year":"2000","unstructured":"American Diabetes Association. Type 2 diabetes in children and adolescents. Pediatrics. 2000;105(36):71\u2013680. https:\/\/doi.org\/10.1542\/peds.105.3.671","journal-title":"Pediatrics."},{"key":"250_CR18","unstructured":"Ajikobe D. Does Nigeria have the most people with diabetes in sub-Saharan Africa? Africa Check Sorting fact from fiction. https:\/\/africacheck.org\/reports\/nigeria-people-diabetes-sub-saharan-africa. Accessed 25 Apr 2020."},{"issue":"5","key":"250_CR19","first-page":"21","volume":"9","author":"A Ajmalahamed","year":"2014","unstructured":"Ajmalahamed A, Nandhini KM, Anand SK. Designing a rule based fuzzy expert controller for early detection and diagnosis of diabetes. ARPN J Eng Appl Sci. 2014;9(5):21\u2013322.","journal-title":"ARPN J Eng Appl Sci."},{"key":"250_CR20","doi-asserted-by":"crossref","unstructured":"Giardina M, Azuaje F, McCullagh P, et al. Supervised learning approach to predicting coronary heart disease complications in type 2 diabetes mellitus patients. In: 6th IEEE symposium on bioinformatics and bioengineering (BIBE'06), Arlington, 2006. p. 325\u201333.","DOI":"10.1109\/BIBE.2006.253297"},{"key":"250_CR21","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1371\/journal.pone.0088225","volume":"9","author":"H Asadi","year":"2014","unstructured":"Asadi H, Dowling R, Yan B, Mitchell P, et al. Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy. PLoS ONE. 2014;9:2.","journal-title":"PLoS ONE"},{"key":"250_CR22","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1097\/ALN.0000000000002374","volume":"129","author":"K Samir","year":"2018","unstructured":"Samir K, Prathamesh K, Andrew DR, et al. Supervised machine-learning predictive analytics for prediction of postinduction hypotension. Anesthesiology. 2018;129:675\u201388.","journal-title":"Anesthesiology"},{"key":"250_CR23","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.ijmedinf.2014.10.002","volume":"84\u20133","author":"W Dai","year":"2015","unstructured":"Dai W, Brisimia TS, Adams WG, Mela T, Saligrama V, Ioannis Ch. Paschalidisa. Int J Med Inform. 2015;84\u20133:189\u201397.","journal-title":"Int J Med Inform"},{"key":"250_CR24","doi-asserted-by":"crossref","unstructured":"Rajagopalan A, Vollmer M. Rapid detection of heart rate fragmentation and cardiac arrhythmias: cycle-by-cycle rr analysis, supervised machine learning model and novel insights. In: Ria\u00f1o D, Wilk S, ten Teije A, editors. Artificial intelligence in medicine. AIME 2019. Lecture notes in computer science. Springer, Cham. 2019. p. 11526.","DOI":"10.1007\/978-3-030-21642-9_47"},{"key":"250_CR25","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1038\/nm843","volume":"9","author":"Q Ye","year":"2003","unstructured":"Ye Q, Qin L, Forgues M, et al. Predicting hepatitis B virus\u2013positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning. Nat Med. 2003;9:416\u201323. https:\/\/doi.org\/10.1038\/nm843.","journal-title":"Nat Med"},{"key":"250_CR26","first-page":"301","volume":"34\u20134","author":"R Daniel","year":"2018","unstructured":"Daniel R, Schrider A, Kern D. Supervised machine learning for population genetics: a new paradigm. Trend Genet. 2018;34\u20134:301\u201312.","journal-title":"Trend Genet"},{"key":"250_CR27","doi-asserted-by":"publisher","first-page":"104068","DOI":"10.1016\/j.ijmedinf.2019.104068","volume":"136","author":"OA Rasheed","year":"2020","unstructured":"Rasheed OA, Mohammed E, Iris S, et al. Comparison of supervised machine learning classification techniques in prediction of locoregional recurrences in early oral tongue cancer. Int J Med Inform. 2020;136:104068.","journal-title":"Int J Med Inform."},{"key":"250_CR28","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1007\/s42979-020-0099-4","volume":"1","author":"NM Mathkunti","year":"2020","unstructured":"Mathkunti NM, Rangaswamy S. Machine learning techniques to identify dementia. SN Comput Sci. 2020;1:118. https:\/\/doi.org\/10.1007\/s42979-020-0099-4.","journal-title":"SN Comput Sci"},{"key":"250_CR29","doi-asserted-by":"crossref","unstructured":"Hussain S, et al. Performance evaluation of various data mining algorithms on road traffic accident dataset. In: Satapathy S, Joshi A, editors. Information and communication technology for intelligent systems. Smart Innovation, Systems and Technologies. 2019. p. 106.","DOI":"10.1007\/978-981-13-1742-2_7"},{"key":"250_CR30","doi-asserted-by":"crossref","unstructured":"Lan H, Pan Y. A crowdsourcing quality prediction model based on random forests. In: 2019 IEEE\/ACIS 18th international conference on computer and information science (ICIS), Beijing, China. 2019. p. 315\u2013319. 10.1109\/ICIS46139.2019.8940306.","DOI":"10.1109\/ICIS46139.2019.8940306"},{"key":"250_CR31","doi-asserted-by":"publisher","first-page":"50118","DOI":"10.1109\/ACCESS.2020.2974764","volume":"8","author":"W Zhang","year":"2020","unstructured":"Zhang W, Chen X, Liu Y. A distributed storage and computation k-nearest neighbor algorithm based cloud-edge computing for cyber-physical-social systems. IEEE Access. 2020;8:50118\u201330. https:\/\/doi.org\/10.1109\/ACCESS.2020.2974764.","journal-title":"IEEE Access."},{"issue":"4","key":"250_CR32","doi-asserted-by":"publisher","first-page":"33","DOI":"10.23919\/CJEE.2019.000025","volume":"5","author":"W Deng","year":"2019","unstructured":"Deng W, Guo Y, Liu J, et al. A missing power data filling method based on improved random forest algorithm. Chin J Electr Eng. 2019;5(4):33\u20139.","journal-title":"Chin J Electr Eng"},{"key":"250_CR33","first-page":"1","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L. Random forests. Mach Learn. 2001;45:1.","journal-title":"Mach Learn"},{"key":"250_CR34","doi-asserted-by":"publisher","first-page":"92893","DOI":"10.1109\/ACCESS.2019.2927602","volume":"7","author":"Y Xia","year":"2019","unstructured":"Xia Y. A novel reject inference model using outlier detection and gradient boosting technique in peer-to-peer lending. IEEE Access. 2019;7:92893\u2013907. https:\/\/doi.org\/10.1109\/ACCESS.2019.2927602.","journal-title":"IEEE Access"},{"key":"250_CR35","doi-asserted-by":"publisher","first-page":"S626","DOI":"10.1002\/gepi.2001.21.s1.s626","volume":"21","author":"K Charles","year":"2001","unstructured":"Charles K, Ingo R, Michael LL, Li H. Sequence analysis using logic regression. Genet Epidemiol. 2001;21:S626\u201331. https:\/\/doi.org\/10.1002\/gepi.2001.21.s1.s626.","journal-title":"Genet Epidemiol"},{"key":"250_CR36","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/B978-0-12-380862-2.00002-3","volume":"72","author":"H Schwender","year":"2010","unstructured":"Schwender H, Ruczinski I. Logic regression and its extensions. Adv Genet. 2010;72:25\u201345.","journal-title":"Adv Genet."},{"key":"250_CR37","unstructured":"Deborah JR. How to interpret a correlation coefficient r. Dummies. https:\/\/www.dummies.com\/education\/math\/statistics\/how-to-interpret-a-correlation-coefficient-r\/. Accessed 12 June 2020."}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-020-00250-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-020-00250-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-020-00250-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T05:59:21Z","timestamp":1744178361000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-020-00250-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,21]]},"references-count":37,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2020,9]]}},"alternative-id":["250"],"URL":"https:\/\/doi.org\/10.1007\/s42979-020-00250-8","relation":{},"ISSN":["2662-995X","2661-8907"],"issn-type":[{"value":"2662-995X","type":"print"},{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,21]]},"assertion":[{"value":"3 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 July 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 July 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"The authors have declared that no conflict of interest exists.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}],"article-number":"240"}}