Evaluation of Diabetic Medications Using Hybrid Fuzzy Pattern Classifier and TOPSIS
Keywords:
Diabetes, Metformin, Anti-diabetic drug, Fuzzy MethodAbstract
Given the plethora of pharmaceuticals available to regulate blood glucose levels, in medical decision-making, choose which ones to take for Type 2 Diabetes is a difficult task. Making decisions is made more difficult by the variety of hyperglycemia-lowering medications, each of which has distinct benefits and potential drawbacks. The study proposes a fuzzy Multi-Criteria Decision-Making model-based computer-aided healthcare decision-making system. This methodology combines the full multiplicative form of the TOPSIS method with Ratio Analysis and a modified version of Fuzzy Multi-Objective Optimization. The goal is to help with the decision-making process while choosing Type 2 Diabetes pharmaceutical therapy. The Fuzzy TOPSIS approach analyzes each option by taking into account all criteria in compliance with a published clinical guideline, On the other hand, while applying the TOPSIS technique to determine the relative relevance of particular criteria, expert opinions are taken into account. In order to address the drawbacks of conventional reference points and improve the ranking process in fuzzy multi-criteria decision-making, this study investigates an extended reference point technique inside the hybrid MCDM paradigm. The principal medicine, DPP-4-I, is confirmed by computational results, and Metformin is recognized as the second-line add-on therapy. The third, fourth, and fifth options are sulfonylurea, glucagon-like peptide1 receptor agonist, and insulin, in that order. To assess the effectiveness of the model, a sensitivity study is carried out by contrasting the outcomes with previous research, different fuzzy MCDM approaches, and an interval TOPSIS method based on observational data. Endocrinologists should be aware of the substantial agreement found between the final anti-diabetic drug rankings produced by the proposed hybrid model and alternative approaches.
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