Comparison of Heart Disease Prediction Using different Machine Learning Algorithms

Abstract

Heart disease is a dreadful threat to the human society that affects globally. Any heart condition and its background should be identified as early as possible to enhance the likelihood of survival. After risks are identified, it helps to anticipate sickness in patients which facilitates a more meaningful, effective, and commercial management of health resources. Low detection accuracy and rising processing complexity are issues with the current research approaches. To overcome such problems. This paper analyses detection and classification of heart disease using different machine learning algorithm. The cause of this work is to hit upon coronary heart illness at early level and keep away from results with the aid of using imposing machine learning algorithm like Naïve Bayes , Decision Tree, Random Forest ,K –Nearest-Neighbor ,Suppoort vector machine(SVM) and logistic regression. The results compared with Precision, Recall, F1 Score and Area Under Curve matrices to understand the efficiency of available algorithms.