Review of Cardiovascular Heart Disease using Data Mining Techniques and Neural Network

Abstract

Cardiovascular Heart Diseases have been the main reason for a large number of deaths in the world over the last few decades and have emerged as the most life-threatening disease not only in India but all over the world. It has been found that the most significant factors for diagnosing heart disease are age, gender, smoking, obesity, diet, physical activity, stress, chest pain type, previous chest pain, blood pressure diastolic, diabetes, ECG, and target. In this paper, the use of expert systems for heart disease using different Neural Network Techniques, including Feedforward Backpropagation, Radial Basis function, Support Vector machine, and Generalized Regression Neural Network, is shown. 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. The Proposed approach could be utilized as an assistant framework to predict cardiovascular heart disease at an early stage.