Improving Heart Disease Prediction Using Feature Selection Approaches

TLDR

Heart disease, a disorder of the heart and blood vessels, remains difficult to predict accurately, and although data science offers promising early prediction tools, current accuracy still requires improvement. The study aims to improve heart disease prediction accuracy by applying feature selection techniques to multiple datasets using data science methods. The authors employed RapidMiner to apply feature selection via Decision Tree, Logistic Regression, Logistic Regression SVM, Naïve Bayes, and Random Forest algorithms across multiple heart disease datasets. These feature selection algorithms yielded improved prediction accuracy for heart disease.

Abstract

Heart Disease is the disorder of heart and blood veins. It is very difficult for medical practitioners and doctors to predict accurate about heart disease diagnosis. Data science is one of the more important things in early prediction and solves large data problems now days. This research paper describes the prediction of heart disease in medical field by using data science. As many researches done research related to that problem but the accuracy of prediction is still needed to be improved. So, this research focuses on feature selection techniques and algorithms where multiple heart disease datasets are used for experimentation analysis and to show the accuracy improvement. By using the Rapid miner as tool; Decision Tree, Logistic Regression, Logistic Regression SVM, Naïve Bayes and Random Forest; algorithms are used as feature selection techniques and improvement is shown in the results by showing the accuracy.