Heart Disease Prediction Based on Machine Learning

In recent years, the Heart disease has become an important cause of death in the world, early detection and treatment of symptoms is a very effective method, while in recent years machine learning technology is also gotten closer to daily life, the combination of machine learning and heart disease prediction, the use of machine learning algorithms for the basic prediction of the patient's disease; through the use of Naive Bayes(NB), Support Vector Machine(SVM), Decision Tree(DT), Random Forest(RF) and K Nearest Neighbors(KNN) by using these algorithms to provide some suggestions to the patients and provide personalised treatment suggestions, so that the patients can get the results in time and have prevention of heart disease; using accuracy to evaluate the results and evaluate each model. In this experiment, the algorithms are implemented and the accuracy of the results of each algorithm is analysed by comparing them horizontally to obtain the optimal algorithm for prediction.