INNOVATIVE EXPERT SYSTEM FOR CORONARY HEART DISEASE DIAGNOSIS UTILIZING SOFT SETS

Abstract

In the recent past, there has been a development of a vast array of theories seeking to explain deal with uncertainties. As a branch of artificial intelligence, medical systems supported with mathematical equations have shown great results in disease diagnosis. Soft set theory is a relatively novel field developed by Molodstov that shows a great deal of potential. In this paper, the "Soft Expert System Framework Incorporating Soft Set Theory and Fuzzy Set Theory" is proposed with the aim of providing a new mathematical tool for handling vagueness in diagnosis. We undertook the development of the Soft Expert System (SES) using medical dataset that contains patient records in order to predict coronary artery disease. In this study, the fuzzy set and soft set were utilized to assess the likelihood of the disease based on the variables including age, blood sugar, high density lipoprotein (HDL), low density lipoprotein (LDL), glycated hemoglobin (HbA1C), uric acid, and blood pressure. The methodology includes key procedures: The generic structure includes Data input, Fuzzyfication, Conversion of Fuzzy sets to soft sets, Reduction of parameters in soft sets, Formulation of soft rules and Data output. This system assumes the role of assisting medical practitioners to improve on their diagnostic abilities and will also ensure that the process takes less time as it would in the normal course