Prioritizing the glucose-lowering medicines for type 2 diabetes by an extended fuzzy decision-making approach with target-based attributes

Different therapeutic classes have been authorized for the treatment of hyperglycemia in type 2 diabetic patients, and even more drug classes are under development. This variety of alternative treatments and the general treatment algorithms of the clinical guidelines lead to a nonuniform prescription of drugs by endocrinologists and diabetic specialists. Diabetes medication choice is a multi-objective problem with many difficulties in making rational decisions because of the wide range of hyperglycemia-lowering agents with multiple benefits and multiple risk elements. This paper proposes a group Entropy-CRiteria Importance Through Inter-criteria Correlation (CRITIC)-Weighted Aggregated Sum Product ASsessment (WASPAS) multi-criteria decision-making (MCDM) model with target-based criteria to prioritize and rank the glucose-lowering medicines for type 2 diabetes using the American Diabetes Association and International Diabetes Federation Clinical Guidelines. The proposed model consists of a weighting method comprising both objective and subjective approaches; the two most common objective approaches (i.e., Entropy and CRITIC methods) are used to find the objective weights. Then, these weights are aggregated with the subjective weights that endocrinologists assign to the criteria. Afterward, a WASPAS target-based method is developed to provide the final ranking of the medications. Finally, the close correlation between the final ranking of the proposed methodology and the average priority order of the medications obtained by different MCDM methods implies the strength and validity of the model performance.

Automated summary

Different therapeutic classes have been authorized for the treatment of hyperglycemia in type 2 diabetic patients. This paper proposes a multi-criteria decision-making model to prioritize and rank the glucose-lowering medicines for type 2 diabetes using clinical guidelines. The model combines objective and subjective approaches to determine the ranking of medications.