Objective To compare and evaluate the effectiveness of four traditional machine learning models in heart disease prediction using UCI heart disease data set.Methods Four machine learning model algorithms,including logistic regression,support vector machine,random forest and decision tree,were used to predict heart disease,and the accuracy,precision,recall and F1 score of the model were calculated to evaluate the performance of the model.Results By calculating metrics such as accuracy,precision,recall,and F1 score,the logistic regression classifier was found to exhibit superior performance in predicting the incidence of heart disease.Conclusion The results show that the logistic regression classifier has good performance in predicting the incidence of heart disease,which can provide reference for related medical practice.
Machine learningHeart disease predictionHeart disease data set
NETL
NSTL
万方数据
