Cardiology Prediction Based on Machine Learning

Abstract

Heart disease is an important disease that endangers human health, with a high mortality rate. Machine learning assisted diagnosis of medical data is a hot topic, and it has made great contributions in predicting patient outcomes and reducing mortality. Therefore, based on the heart disease index data, this paper uses Decision tree model, Clustering model, and Naive Bayes model to predict whether or not having heart disease. The results show that the Naive Bayes algorithm has better prediction accuracy and can assist doctors in diagnosis and treatment.