Machine Learning Based Classification for Heart Disease Prognosis - Divya Lalita Sri Jalligampala, Dr. R. V. S. Lalitha, A. Phani Sridhar, S. S. V. S. R. Kumar Pullela (International Journal of Advanced Science and Technology, 2020)
Today Heart Disease is one of the major & leading threats to the people which leads to death globally. Even medical practitioners are not able to predict it easily, which automatically creates a demand for the efficient classification of heart disease prediction. But it is a complex task because of health care system generates a huge amount of data from multiple sources in multiple ways. So there is a need to develop a system to perform classification of Heart Disease forecasting. For these Machine Learning algorithms were used. In this research, various Supervised Machine Learning algorithms like Decision Tree, Support Vector Machine, Logistic Regression, Multilayer Perceptron, Random Forest, and Naïve Bayes are applied on the given dataset to classify Heart Disease Prediction and their performance is measured in terms of Accuracy, Precision, and Recall. Based on the outcomes, the Random forest algorithm produces better accuracy than the other algorithms used.