Prediction of Heart Disease using Machine Learning Algorithms with Feature Selection Techniques

Abstract

Data is an asset in the digital era, and enormous data was generating day by day in all the fields, including the healthcare industry. The data on the healthcare industry data consists of personal information and disease-related information about a patient and stored in various formats and units. Machine learning and Artificial Intelligence techniques will help us analyze the voluminous amount of data to identify the hidden patterns of a specific disease from the healthcare data and help us predict a particular disease in the future. In this paper, we proposed a decision support system to predict heart disease, especially cardiovascular disease, through machine learning algorithms. This system experimented with the reduced set feature of the UCI Machine learning repository dataset using a linear kernel-based support vector machine algorithm. This system has also compared it with other machine learning algorithms such as K-Nearest Neighbours, Decision tree, and Random forest in Python. All four machine learning algorithms' performance has been evaluated based on accuracy, misclassification rate, precision, recall, and f-score value. From the experimental results, SVM with a linear kernel function classification algorithm produces better accuracy of 95.08% compared with others for predicting heart disease.