Diagnosis of Type-2 Diabetes using Multi-Criteria Decision-Making Techniques with Diverse Operators

This study applies multi-criteria decision-making (MCDM) techniques to identify and evaluate the risk factors associated with Type 2 diabetes mellitus (T2DM). By leveraging advanced MCDM approaches, the study aims to enhance the decision-making process for T2DM diagnosis and management. The primary objective of this study is to develop a comprehensive framework for ranking and prioritizing various risk factors contributing to the onset and progression of T2DM. The study also aims to compare different MCDM techniques and aggregation operators to provide a deeper understanding of the influence of key factors such as genetics, lifestyle, and environmental triggers. The study integrates four advanced MCDM approaches: Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE-II), Graph Theory and Matrix Approach (GTMA), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and VIˇsekriterijumsko KOmpromisno Rangiranje (VIKOR). A diverse range of aggregation operators is utilized within the MCDM framework to enhance the evaluation process, including the Einstein aggregation operator, Hamy mean operator, Dombi operator, and arithmetic-geometric aggregation operators. These operators facilitate a nuanced assessment of risk factors, allowing for a more comprehensive and structured evaluation of their impact on Type 2 diabetes mellitus (T2DM). The study provides a detailed comparison between PROMETHEE-II, GTMA, TOPSIS, and VIKOR rankings. Integrating various aggregation operators highlights the relative significance of different risk factors. The results demonstrate how different methodologies influence the prioritization of risk factors, contributing to a more refined approach to decision-making in T2DM management. This study enhances decision-making processes for T2DM diagnosis and management by offering a robust, operator-driven evaluation system for healthcare professionals. The comparative analysis of MCDM techniques and aggregation operators provides valuable insights into the key risk factors affecting T2DM, ultimately aiding in better healthcare strategies and preventive measures.