{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T12:21:54Z","timestamp":1773922914399,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T00:00:00Z","timestamp":1710288000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Obesity is increasingly becoming a prevalent health concern among adolescents, leading to significant risks like cardiometabolic diseases (CMDs). The early discovery and diagnosis of CMD is essential for better outcomes. This study aims to build a reliable artificial intelligence model that can predict CMD using various machine learning techniques. Support vector machines (SVMs), K-Nearest neighbor (KNN), Logistic Regression (LR), Random Forest (RF), and Gradient Boosting are five robust classifiers that are compared in this study. A novel \u201crisk level\u201d feature, derived through fuzzy logic applied to the Conicity Index, as a novel feature, which was previously unused, is introduced to enhance the interpretability and discriminatory properties of the proposed models. As the Conicity Index scores indicate CMD risk, two separate models are developed to address each gender individually. The performance of the proposed models is assessed using two datasets obtained from 295 records of undergraduate students in Saudi Arabia. The dataset comprises 121 male and 174 female students with diverse risk levels. Notably, Logistic Regression emerges as the top performer among males, achieving an accuracy score of 91%, while Gradient Boosting lags with a score of 72%. Among females, both Support Vector Machine and Logistic Regression lead with an accuracy score of 87%, while Random Forest performs least optimally with a score of 80%.<\/jats:p>","DOI":"10.3390\/bdcc8030031","type":"journal-article","created":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T13:08:43Z","timestamp":1710335323000},"page":"31","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Machine Learning Approaches for Predicting Risk of Cardiometabolic Disease among University Students"],"prefix":"10.3390","volume":"8","author":[{"given":"Dhiaa","family":"Musleh","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0161-3200","authenticated-orcid":false,"given":"Ali","family":"Alkhwaja","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4637-3298","authenticated-orcid":false,"given":"Ibrahim","family":"Alkhwaja","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4273-9791","authenticated-orcid":false,"given":"Mohammed","family":"Alghamdi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6052-5297","authenticated-orcid":false,"given":"Hussam","family":"Abahussain","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4912-2236","authenticated-orcid":false,"given":"Mohammed","family":"Albugami","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}]},{"given":"Faisal","family":"Alfawaz","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8145-2775","authenticated-orcid":false,"given":"Said","family":"El-Ashker","sequence":"additional","affiliation":[{"name":"Self-Development Department, Deanship of Preparatory Year, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9807-4870","authenticated-orcid":false,"given":"Mohammed","family":"Al-Hariri","sequence":"additional","affiliation":[{"name":"Department of Physiology, College of Medicine, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,13]]},"reference":[{"key":"ref_1","first-page":"903","article-title":"The Prevalence and Risk Factors of Obesity among Medical Students at Shaqra University, Saudi Arabia","volume":"10","author":"Ahmad","year":"2020","journal-title":"Ann. Med. Health Sci. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"0036850421998532","DOI":"10.1177\/0036850421998532","article-title":"Adiposity and Cardiometabolic Risk Assessment among University Students in Saudi Arabia","volume":"104","author":"Albaker","year":"2021","journal-title":"Sci. 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