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This paper aims to support nondiabetic individuals by helping them understand potential risk factors associated with developing diabetes, thereby enabling them to take preventive measures. This study highlights the crucial need for explainable artificial intelligence by presenting a pragmatic approach to generate proximity\u2010constrained counterfactuals for assessing diabetic risk. By synergistically combining the capabilities of Local Interpretable Model\u2010Agnostic Explanations and Diverse Counterfactual Explanations, our methodology focuses on perturbing the most influential features identified by different AI explainers to reverse decisions while maintaining proximity to the decision boundary. The resulting counterfactuals offer actionable insights, enabling individuals to actively manage lifestyle choices and potentially reduce their diabetes risk. Our findings demonstrate the effective implementation of this strategy, underscoring its practicality and user\u2010oriented decision\u2010support capabilities. It addresses constraints and integrates human assessments for a more comprehensive evaluation. The proposed approach contributes to enhancing trust and transparency in machine learning models for diabetic risk prediction through interpretable and actionable counterfactual explanations.<\/jats:p>","DOI":"10.1155\/acis\/3424976","type":"journal-article","created":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T06:18:36Z","timestamp":1766125116000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Proximity\u2010Constrained Counterfactuals for Explainable Diabetes Risk Assessment"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6088-9487","authenticated-orcid":false,"given":"Md Faisal","family":"Kabir","sequence":"first","affiliation":[]},{"given":"Abhishek Mandar","family":"Mahakal","sequence":"additional","affiliation":[]},{"given":"Yucheng","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1506-7924","authenticated-orcid":false,"given":"Anas","family":"AlSobeh","sequence":"additional","affiliation":[]},{"given":"Bilal","family":"Al\u2010Ahmad","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2413-7074","authenticated-orcid":false,"given":"Rami S.","family":"Alkhawaldah","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2025,12,18]]},"reference":[{"key":"e_1_2_11_1_2","volume-title":"Health Information on Diabetes","author":"National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)","year":"2023"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1504\/ijcat.2019.097118"},{"key":"e_1_2_11_3_2","volume-title":"IDF Diabetes Atlas","author":"International Diabetes Federation","year":"2019"},{"key":"e_1_2_11_4_2","volume-title":"Your Diabetes Care and Management Plan","author":"American Diabetes Association","year":"2025"},{"key":"e_1_2_11_5_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aaa8415"},{"key":"e_1_2_11_6_2","first-page":"436","article-title":"Deep learning","volume":"521","author":"Yann L. 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