Abstract： Objective To explore the prediction of heart diseases using machine learning-based methods,including decision trees(DT),random forest(RF),support vector machine(SVM),K-nearest neighbors(KNN),and naive Bayes(NB).Methods The Cleveland heart disease dataset was utilized as the data source.Significant features were selected using Pearson correlation coefficients.Heart disease prediction models were constructed using DT,RF,SVM,KNN,and NB algorithms,separately,and the model performance was evaluated with multiple metrics,including accuracy,precision,recall rate,F1 score,and AUC value.Results The study included 303 samples,and among the 13 clinical features,11 were found to be significant.RF prediction model achieved the highest accuracy(0.869),recall rate(0.906),F1 score(0.879),and AUC value(0.93),while NB prediction model obtained the highest precision(0.900).Conclusion Machine learning-based methods are promising in heart disease prediction,with the RF prediction model demonstrating significant advantages and NB prediction model exhibiting satisfactory performance.