{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T04:31:14Z","timestamp":1786509074305,"version":"build-2736575974"},"reference-count":45,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T00:00:00Z","timestamp":1786320000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Faisal University","award":["KFU263605"],"award-info":[{"award-number":["KFU263605"]}]},{"award":["KFU263605"],"award-info":[{"award-number":["KFU263605"]}],"id":[{"id":"https:\/\/ror.org\/00dn43547","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Pers. Med.","JPM"],"abstract":"<jats:p>Background: Diabetes mellitus affects over 500 million people worldwide, yet many machine learning prediction models remain difficult to interpret, limiting their clinical applicability. This study proposes an explainable machine learning framework integrating the Marine Predators Algorithm (MPA) for hyperparameter optimization with SHAP-based explainability to diabetes prediction. Methods: Logistic Regression (LR), Random Forest (RF), and MPA-optimized XGBoost were evaluated using a publicly available Kaggle diabetes dataset of approximately 100,000 records. Statistical significance was assessed using Wilcoxon signed-rank tests with Bonferroni correction for fold-wise cross-validation results, while McNemar\u2019s and DeLong\u2019s tests were employed for paired comparison of independent test-set predictions and ROC-AUC values, respectively. Performance was assessed using accuracy, precision, recall, F1-score, specificity, ROC-AUC, and Brier score. SHAP was used to provide global and local model explanations. Results: The MPA-optimized XGBoost model achieved the highest performance, with 96.72% accuracy, 97.70% precision, 95.70% recall, 96.71% F1-score, and 99.56% ROC-AUC, significantly outperforming LR and RF (p &lt; 0.001). The model demonstrated good calibration with a Brier score of 0.0262. SHAP analysis identified HbA1c level, blood glucose level, and age as the most influential predictors, while interaction analysis indicated a synergistic relationship between HbA1c and blood glucose. Conclusions: The proposed framework demonstrated strong predictive performance and interpretable model behavior on the publicly available diabetes dataset used in this study. These findings indicate the potential of MPA-based optimization combined with SHAP explainability for supporting transparent machine learning research in diabetes prediction. However, additional external validation using independent clinical datasets is required before considering the framework for clinical decision support or real-world deployment.<\/jats:p>","DOI":"10.3390\/jpm16080423","type":"journal-article","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T15:10:24Z","timestamp":1786374624000},"page":"423","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Optimized and Explainable Machine Learning Framework for Diabetes Prediction Using Marine Predators Algorithm and SHAP"],"prefix":"10.3390","volume":"16","author":[{"given":"Alifa","family":"Nasrin","sequence":"first","affiliation":[{"name":"Combined Military Hospital (CMH), Chattagram 4220, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Bin","family":"Asif","sequence":"additional","affiliation":[{"name":"Clinical Department, Azerbaijani Medical University, Baku AZ1022, Azerbaijan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0793-7763","authenticated-orcid":false,"given":"Ramasamy","family":"Naidu","sequence":"additional","affiliation":[{"name":"Computer Engineering, CCSIT, King Faisal University, Al Ahsa 31982, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Afzal Haq","family":"Asif","sequence":"additional","affiliation":[{"name":"Pharmacy Practice, College of Clinical Pharmacy, King Faisal University, Al-Ahsa 31982, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6539-7334","authenticated-orcid":false,"given":"Muhammad Shahzad","family":"Chohan","sequence":"additional","affiliation":[{"name":"Pharmacy Practice, College of Clinical Pharmacy, King Faisal University, Al-Ahsa 31982, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gausal Azam","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Clinical Nutrition, College of Applied Medical Sciences, King Faisal University, Al-Ahsa 31982, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5069-0447","authenticated-orcid":false,"given":"Md","family":"Arifuzzaman","sequence":"additional","affiliation":[{"name":"Civil and Environmental Engineering, College of Engineering,  King Faisal University, Al-Ahsa 31982, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akm","family":"Azad","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3420-3597","authenticated-orcid":false,"given":"Muhammad Ali","family":"Martuza","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,10]]},"reference":[{"key":"ref_1","first-page":"6521532","article-title":"Voting Classification-Based Diabetes Mellitus Prediction Using Hypertuned Machine-Learning Techniques","volume":"2022","author":"Mushtaq","year":"2022","journal-title":"Mob. 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