Performance Analysis of Classification Methods in the Diagnosis of Heart Disease

Abstract

With a mortality rate of 17.9 million per year, heart disease has emerged out to be the deadliest disease of the world. Early detection of this disease can reduce mortality. Data mining based disease diagnosis systems can aid medical professionals in the correct and timely diagnosis of the disease. In this study a Python-based data mining system, capable of diagnosing the heart disease using decision tree, KNN classifier, naive Bayes, random forest, and support vector machine (SVM) classification data mining methods, has been developed. The system was applied to four heart disease datasets obtained from the UCI machine learning repository. The relative performance of various data mining techniques was evaluated by comparing the results. The results showed that the support vector machine, with 98.7% efficiency, 98.4% precision, and 99.2% recall, has emerged out to be the best method for the diagnosis of heart disease.