Analysis of Supervised Machine Learning Algorithms for Heart Disease Prediction with Reduced Number of Attributes using Principal Component Analysis

Abstract

Globally, research on causes of death due to heart disease has shown that it is the number one cause of death. If current trends are allowed to continue, 23.6 million people will die from heart disease in coming 2030. The healthcare industry gathers enormous amounts of heart disease data which unfortunately, are not "mined" to discover hidden information for effective decision making. In this paper, study of PCA has been done which finds the minimum number of attributes required to increase the accuracy of various supervised machine learning algorithms. The objective of this research is to analyze supervised machine learning algorithms to predict heart disease.