{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:13:41Z","timestamp":1774628021923,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,12,26]],"date-time":"2024-12-26T00:00:00Z","timestamp":1735171200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Diabetes is a global health challenge that requires early detection for effective management. This study integrates Automated Machine Learning (AutoML) with Explainable Artificial Intelligence (XAI) to improve diabetes risk prediction and enhance model interpretability for healthcare professionals. Using the Pima Indian Diabetes dataset, we developed an ensemble model with 85.01% accuracy leveraging AutoGluon\u2019s AutoML framework. To address the \u201cblack-box\u201d nature of machine learning, we applied XAI techniques, including SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), Integrated Gradients (IG), Attention Mechanism (AM), and Counterfactual Analysis (CA), providing both global and patient-specific insights into critical risk factors such as glucose and BMI. These methods enable transparent and actionable predictions, supporting clinical decision-making. An interactive Streamlit application was developed to allow clinicians to explore feature importance and test hypothetical scenarios. Cross-validation confirmed the model\u2019s robust performance across diverse datasets. This study demonstrates the integration of AutoML with XAI as a pathway to achieving accurate, interpretable models that foster transparency and trust while supporting actionable clinical decisions.<\/jats:p>","DOI":"10.3390\/info16010007","type":"journal-article","created":{"date-parts":[[2024,12,26]],"date-time":"2024-12-26T21:13:18Z","timestamp":1735247598000},"page":"7","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Towards Transparent Diabetes Prediction: Combining AutoML and Explainable AI for Improved Clinical Insights"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8089-837X","authenticated-orcid":false,"given":"Raza","family":"Hasan","sequence":"first","affiliation":[{"name":"Department of Computer Science, Solent University, Southampton SO14 0YN, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0661-1174","authenticated-orcid":false,"given":"Vishal","family":"Dattana","sequence":"additional","affiliation":[{"name":"Digital Transformation, Oman College of Management & Technology, P.O. Box 680, Barka 320, Oman"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2860-4095","authenticated-orcid":false,"given":"Salman","family":"Mahmood","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Nazeer Hussain University, ST-2, near Karimabad, Karachi 75950, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0766-2402","authenticated-orcid":false,"given":"Saqib","family":"Hussain","sequence":"additional","affiliation":[{"name":"Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8QH, UK"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/978-981-19-7455-7_2","article-title":"An Explainable AI Approach for Diabetes Prediction","volume":"565","author":"Jakka","year":"2023","journal-title":"Innov. Comput. Sci. 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