Combining Fuzzy MCDM and Machine Learning for Predictive Analytics in Diabetes Management

This study utilizes the Fuzzy Analytic Hierarchy Process (FAHP) to evaluate the factors influencing the risk of diabetes-related complications. Data was collected from diabetes specialists in Coimbatore, each with over 15 years of experience, to construct a pairwise comparison matrix based on expert opinions. The analysis identified six key criteria: Age (C1), Cardiopulmonary function (C2), Cardiovascular disease (C3), Family history of sudden death (C4), Smoking (C5), and Blood glucose (C6). The results reveal that Cardiopulmonary function (C2) is the most critical factor, with a weight of 0.1193, followed by Age (C1) and Family history of sudden death (C4). While long-term health indicators are more influential than immediate lifestyle factors, the contributions of Smoking (C5) and Alcoholism (C6) are still relevant. These findings emphasize the need for a comprehensive approach to diabetes management that balances inherent and lifestyle-related risks.