Abstract

The heart is the coordinating centre of the major endocrine glandular structure of the body, which produces hormones that profoundly affect the operations of the body, and diagnosing cardiovascular disease is a difficult but critical task. By extracting knowledge and information about the disease from patient data, data mining is a more practical technique to help doctors detect disorders. We use a variety of machine learning methods here, including logistic regression and support vector classifiers (SVC), K-nearest neighbours Classifier (KNN), Decision Tree Classifier, Random Forest Classifier and Gradient Boosting Classifier. These algorithms are applied to patient’s data containing 13 different factors to build a system that predicts heart disease in less time with more accuracy.