HeartHealth: An Intelligent Model for Multi-Attribute Based Heart Condition Monitoring using Fuzzy-TOPSIS Method

Cardiovascular infections (CVDs) are the number 1 reason for death around the world, taking an expected 17.9 million lives every year. CVDs are a gathering of issues of the heart, veins which incorporate coronary illness, cerebrovascular infection, rheumatic coronary illness, and different conditions. In this work, we used a multi-criteria-based Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to permit compromises between rules, where a weak outcome on one basis can be invalidated by a decent outcome in another rule. This gives a more practical type of display than non-compensatory strategies, which incorporate or reject elective arrangements dependent on hard cutoffs. This results in a weighted approach from different sources of the correct value and measures the ideal distance from our subject’s heart condition. For the different sources, we have used feature weights computed using PCA, LDA, and Permutation Feature importance over the Cleveland Heart dataset and used those for generating the TOPSIS model.