{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T18:24:27Z","timestamp":1772043867982,"version":"3.50.1"},"reference-count":20,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T00:00:00Z","timestamp":1683676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Faculty of Science, Silpakorn University","award":["SRIF-JRG-2566-06"],"award-info":[{"award-number":["SRIF-JRG-2566-06"]}]},{"name":"Silpakorn University Research, Innovation and Creativity Administration Office (SURIC)","award":["SRIF-JRG-2566-06"],"award-info":[{"award-number":["SRIF-JRG-2566-06"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>Machine learning techniques play an increasingly prominent role in medical diagnosis. With the use of these techniques, patients\u2019 data can be analyzed to find patterns or facts that are difficult to explain, making diagnoses more reliable and convenient. The purpose of this research was to compare the efficiency of diabetic classification models using four machine learning techniques: decision trees, random forests, support vector machines, and K-nearest neighbors. In addition, new diabetic classification models are proposed that incorporate hyperparameter tuning and the addition of some interaction terms into the models. These models were evaluated based on accuracy, precision, recall, and the F1-score. The results of this study show that the proposed models with interaction terms have better classification performance than those without interaction terms for all four machine learning techniques. Among the proposed models with interaction terms, random forest classifiers had the best performance, with 97.5% accuracy, 97.4% precision, 96.6% recall, and a 97% F1-score. The findings from this study can be further developed into a program that can effectively screen potential diabetes patients.<\/jats:p>","DOI":"10.3390\/computation11050096","type":"journal-article","created":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T04:29:01Z","timestamp":1683779341000},"page":"96","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Diabetes Classification Using Machine Learning Techniques"],"prefix":"10.3390","volume":"11","author":[{"given":"Methaporn","family":"Phongying","sequence":"first","affiliation":[{"name":"Department of Statistics, Faculty of Science, Silpakorn University, Nakhon Pathom 73000, Thailand"}]},{"given":"Sasiprapa","family":"Hiriote","sequence":"additional","affiliation":[{"name":"Department of Statistics, Faculty of Science, Silpakorn University, Nakhon Pathom 73000, Thailand"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,10]]},"reference":[{"key":"ref_1","unstructured":"(2023, April 29). Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/diabetes."},{"key":"ref_2","unstructured":"(2023, April 29). Available online: https:\/\/www.cdc.gov\/diabetes\/library\/spotlights\/diabetes-facts-stats.html."},{"key":"ref_3","unstructured":"Griffin, P., and Rodgers, M.D. (2023, April 14). Type 1 Diabetes. National Institute of Diabetes and Digestive and Kidney Diseases, Available online: https:\/\/www.niddk.nih.gov\/health-information\/diabetes\/overview\/what-is-diabetes\/type-1-diabetes."},{"key":"ref_4","unstructured":"Griffin, P., and Rodgers, M.D. (2023, April 14). Risk Factors for Type 2 Diabetes. National Institute of Diabetes and Digestive and Kidney Diseases, Available online: https:\/\/www.niddk.nih.gov\/health-information\/diabetes\/overview\/risk-factors-type-2-diabetes."},{"key":"ref_5","unstructured":"(2023, April 29). Available online: https:\/\/www.cdc.gov\/diabetes\/basics\/risk-factors.html."},{"key":"ref_6","unstructured":"Pacharawongsakda, E. (2014). An Introduction to Data Mining Techniques, Pearson Education."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wei, S., Zhao, X., and Miao, C. (2018, January 5\u20138). A comprehensive exploration to the machine learning techniques for diabetes identification. Proceedings of the 2018 IEEE 4th World Forum on Internet of Things (WF-IoT), Singapore.","DOI":"10.1109\/WF-IoT.2018.8355130"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"515","DOI":"10.3389\/fgene.2018.00515","article-title":"Predicting Diabetes Mellitus with Machine Learning Techniques","volume":"9","author":"Zou","year":"2018","journal-title":"Front Genet."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s40537-019-0175-6","article-title":"Analysis of diabetes mellitus for early prediction using optimal features selection","volume":"6","author":"Sneha","year":"2019","journal-title":"J. Big Data"},{"key":"ref_10","unstructured":"(2023, April 29). International Statistical Classification of Diseases and Related Health Problems 10th Revision. Available online: https:\/\/icd.who.int\/browse10\/2019\/en#\/E10-E14."},{"key":"ref_11","unstructured":"(2023, April 29). Available online: https:\/\/en.wikipedia.org\/wiki\/Information_gain_ratio#References."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Changpetch, P., Pitpeng, A., Hiriote, S., and Yuangyai, C. (2021). Integrating Data Mining Techniques for Na\u00efve Bayes Classification: Applications to Medical Datasets. Computation, 9.","DOI":"10.3390\/computation9090099"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1749","DOI":"10.2337\/dc10-2424","article-title":"Correlates of quality of life in older adults with diabetes: The Diabetes & Aging Study","volume":"34","author":"Laiteerapong","year":"2011","journal-title":"Diabetes Care"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1001\/jama.2021.12531","article-title":"Screening for Prediabetes and Type 2 Diabetes: US Preventive Services Task Force Recommendation Stateme","volume":"326","author":"Davidson","year":"2021","journal-title":"JAMA"},{"key":"ref_15","unstructured":"Deepti, S., and Dilip, S.S. (2018, January 7\u20138). Prediction of Diabetes using Classification Algorithms. Proceedings of the International Conference on Computational Intelligence and Data Science (ICCIDS 2018), Gurugram, India."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hafeez, M.A., Rashid, M., Tariq, H., Abideen, Z.U., Alotaibi, S.S., and Sinky, M.H. (2021). Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm. Appl. Sci., 11.","DOI":"10.3390\/app11156728"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.30534\/ijeter\/2020\/32852020","article-title":"Comparison of Accuracy Level of Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) Algorithms in Predicting Heart Disease","volume":"8","author":"Dimas","year":"2020","journal-title":"Int. J. Emerg. Trends Eng. Res."},{"key":"ref_18","unstructured":"Maneerat, P. (2023, April 14). WEKA Data Mining Program. Available online: https:\/\/maneerat-paranan.blogspot.com\/2012\/02\/weka.html."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.inffus.2021.02.015","article-title":"Risk Prediction of Diabetes: Big data mining with fusion of multifarious physical examination indicators","volume":"75","author":"Yang","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"833","DOI":"10.2337\/dc15-2251","article-title":"Metabolomics in Prediabetes and Diabetes: A Systematic Review and Meta-analysis","volume":"39","author":"Hruby","year":"2016","journal-title":"Diabetes Care"}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/11\/5\/96\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:32:16Z","timestamp":1760124736000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/11\/5\/96"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,10]]},"references-count":20,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["computation11050096"],"URL":"https:\/\/doi.org\/10.3390\/computation11050096","relation":{},"ISSN":["2079-3197"],"issn-type":[{"value":"2079-3197","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,10]]}}}