Analysis and Study of Expert System for Heart Disease using Artificial Neural Network and Data Mining Techniques

Abstract

In today's culture, heart disease is a significant contributor to morbidity and death. Medical diagnosis is a crucial yet challenging process that needs to be completed precisely and effectively. The practical use of expert systems for heart disease employing several approaches, including Support Vector Machine, Radial Basis function, GRNN, and Feedforward Backpropagation, is shown in this study. In order to construct any kind of expert system based on symptoms, it is important to understand how data should be gathered and which factors are helpful. Many different kinds of strategies that are employed in constructing the expert system with technical features are among the factors highlighted. Another suggestion made in this article was to use data mining, Support Vector Machines (SVM), Genetic Algorithms, Rough Set Theory, Association Rules, and Neural Networks to identify cardiac disorders. We briefly looked at the effectiveness of the Decision tree and RBF for treating heart disease in this study compared to the other strategies. As a result, it is noted that data mining may aid in the detection or prognosis of high or low-risk cardiac illnesses.