Efficacy of machine learning algorithms for heart disease prediction
10.3969/j.issn.1005-202X.2024.07.018
- VernacularTitle:机器学习算法对心脏病预测效能的研究
- Author:
Meiyan JIANG
1
;
Hui ZHANG
Author Information
1. 广东医科大学第一临床医学院,广东湛江 524023;广东医科大学广东省第二人民医院麻醉科,广东广州 510317
- Keywords:
machine learning;
heart disease prediction;
medical big data
- From:
Chinese Journal of Medical Physics
2024;41(7):905-909
- CountryChina
- Language:Chinese
-
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.
