A prediction of heart disease using machine learning algorithms

Abstract

Now a day’s heart disease is emerging as one of the most death-dealing diseases. As per a report published by the World Health Organization [WHO], heart disease is one of the most hazardous diseases to human which causes death all over the world from the last 20 years. Approx. 12 million people are dying every year, which makes it the biggest challenge for medical professionals to develop an early diagnosis of heart disease with better accuracy. In this paper, we have applied different machine learning algorithms and compared their classification accuracies. We have proposed a modified algorithm using logistic regression with principal component analysis for predicting heart disease with more accuracy on various attributes such as age, blood pressure, chest pain, serum cholesterol levels, heart rate, and other characteristic attributes, and patients will be classified according to varying degrees of coronary artery disease.