An Enhanced Fuzzy TOPSIS in Soft Computing for the Best Selection of Health Insurance

Abstract

Among several computing models in Health Insurance, the selection of appropriate health insurance is necessary for gaining higher beneficial profit during critical situations. Sometimes the random selection of Health Insurance leads to bad results. In this paper, we proposed the Multiple Criteria Decision Making (MCDM) theory to make a decision about the best form of health insurance. This MCDM is one of the best ideal solutions to overcome bad results. We implemented TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method for choosing the best Health Insurance. In the TOPSIS method, we have defined the Fuzzy TOPSIS hesitant method for a decision-making scenario for multi-criteria. The advantages of hesitant fuzzy TOPSIS approaches are flexibility, consistency, understandability, strong analytical efficiency and the ability to calculate the relative strength in a simple mathematical form for each alternative. In this, we used linguistic and Intuitionistic decision-makers. Here multiple objectives like 1.beneficial and non-beneficial 2.fuzzy weightage for attributes are used for group decision making. In the Health Insurance scenario we have multi attributes like 1.individual plan 2.family plan 3.entry age 4.premium 5.claim 6.sum assured that are applied to all Health Insurance and by using the above methods.