Heart Disease Prediction System Based on Multiple Feature Selection Algorithm with Ensemble Classifier

Abstract

In today's world, deaths from heart disease have become a significant problem. Nearly one person dies from heart disease every minute. In most situations, the diagnosis of cardiac disease is based on a complicated mix of clinical and pathological data. This complexity leads to expensive medical costs, which have an impact on the quality of medical care. The intention of the work is to predict the heart disease based on medical records. The proposed method employs data mining classification methods like KNN, SVM, and Decision Tree to find out knowledge in a heart disease dataset, which is obtained from UCI Machine learning repository. The input dataset consists of 282 observations with 75 attributes. The feature selection of input dataset is done using Multiple Feature Selection Algorithm (MFSA), which uses Auto-Correlation and Information gain to find the best set of features and improves the performance of the classifiers. The results are obtained from classification methods with and without using Feature Selection in the form of accuracy. Based on the experimental analysis, classification with Feature Selection method outperformed classification without Feature Selection method.