{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T16:34:10Z","timestamp":1776184450499,"version":"3.50.1"},"reference-count":71,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,7,2]],"date-time":"2023-07-02T00:00:00Z","timestamp":1688256000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Imam Mohammad Ibn Saud Islamic University (IMSIU)","award":["RP-21-09-09"],"award-info":[{"award-number":["RP-21-09-09"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Diabetes is a chronic disease caused by a persistently high blood sugar level, causing other chronic diseases, including cardiovascular, kidney, eye, and nerve damage. Prompt detection plays a vital role in reducing the risk and severity associated with diabetes, and identifying key risk factors can help individuals become more mindful of their lifestyles. In this study, we conducted a questionnaire-based survey utilizing standard diabetes risk variables to examine the prevalence of diabetes in Bangladesh. To enable prompt detection of diabetes, we compared different machine learning techniques and proposed an ensemble-based machine learning framework that incorporated algorithms such as decision tree, random forest, and extreme gradient boost algorithms. In order to address class imbalance within the dataset, we initially applied the synthetic minority oversampling technique (SMOTE) and random oversampling (ROS) techniques. We evaluated the performance of various classifiers, including decision tree (DT), logistic regression (LR), support vector machine (SVM), gradient boost (GB), extreme gradient boost (XGBoost), random forest (RF), and ensemble technique (ET), on our diabetes datasets. Our experimental results showed that the ET outperformed other classifiers; to further enhance its effectiveness, we fine-tuned and evaluated the hyperparameters of the ET. Using statistical and machine learning techniques, we also ranked features and identified that age, extreme thirst, and diabetes in the family are significant features that prove instrumental in the detection of diabetes patients. This method has great potential for clinicians to effectively identify individuals at risk of diabetes, facilitating timely intervention and care.<\/jats:p>","DOI":"10.3390\/info14070376","type":"journal-article","created":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T00:49:27Z","timestamp":1688345367000},"page":"376","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["A Comparison of Machine Learning Techniques for the Detection of Type-2 Diabetes Mellitus: Experiences from Bangladesh"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8483-7059","authenticated-orcid":false,"given":"Md. Jamal","family":"Uddin","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1640-6649","authenticated-orcid":false,"given":"Md. Martuza","family":"Ahamad","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8392-2666","authenticated-orcid":false,"given":"Md. Nesarul","family":"Hoque","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6001-8374","authenticated-orcid":false,"given":"Md. Abul Ala","family":"Walid","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bangladesh Army University of Engineering & Technology (BAUET), Natore 6431, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8866-743X","authenticated-orcid":false,"given":"Sakifa","family":"Aktar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6789-3363","authenticated-orcid":false,"given":"Naif","family":"Alotaibi","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5507-9399","authenticated-orcid":false,"given":"Salem A.","family":"Alyami","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6798-6535","authenticated-orcid":false,"given":"Muhammad Ashad","family":"Kabir","sequence":"additional","affiliation":[{"name":"School of Computing, Mathematics, and Engineering, Charles Sturt University, Bathurst, NSW 2795, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0756-1006","authenticated-orcid":false,"given":"Mohammad Ali","family":"Moni","sequence":"additional","affiliation":[{"name":"Artificial Intelligence & Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD 4072, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S81","DOI":"10.2337\/dc14-S081","article-title":"Diagnosis and classification of diabetes mellitus","volume":"37","author":"Association","year":"2014","journal-title":"Diabetes Care"},{"key":"ref_2","unstructured":"IDF (2023, May 07). 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