Hybrid Fuzzy Pattern Classifier and ARAS for Evaluating Type 2 Diabetes Medications: A Computer-Aided Decision-Making Approach

Abstract

Effectively managing blood glucose levels in Type 2 Diabetes requires selecting medications with care, given the variety of available drugs, each with its own pros and cons. To get around this complexity, a study suggests using a fuzzy Multi-Criteria Decision-Making (MCDM) model-based approach to assist healthcare decision-making. This technique combines the multiplicative Additive Ratio Assessment (ARAS) approach with Ratio Analysis and a modified version of Fuzzy Multi-Objective Optimization. By integrating these methods, the system aims to offer a systematic and effective way to choose the most suitable medications for Type 2 Diabetes, considering factors like effectiveness, safety, cost, and patient preferences. This method shows potential in improving healthcare decision-making for personalized diabetes management, leading to better patient outcomes and quality of life. Integrating these advanced decision-making techniques simplifies and enhances the process of selecting the most appropriate pharmaceutical therapy for Type 2 Diabetes. This helps healthcare professionals make more informed decisions by balancing efficacy, safety, and other important factors. The Fuzzy ARAS approach evaluates each pharmaceutical option based on relevant criteria and expert opinions, ensuring a comprehensive assessment. To enhance the decision-making process, the study explores an extended reference point technique within the MCDM framework. The objective of this approach is to enhance the precision and dependability of the pharmacological medication selection process for Type 2 Diabetes by merging clinical guidelines, professional opinions, and sophisticated analytical techniques. Based on the computational results, it appears that DPP-4 inhibitors are the main treatment, while metformin is the recommended add-on drug for second line. Sulfonylureas are ranked third, glucagon-like peptide-1 receptor agonists are ranked fourth, and insulin is ranked fifth. A sensitivity analysis confirms the model's effectiveness, showing agreement with alternative methods in ranking anti-diabetic drugs.