Key result
A Random Forest algorithm predicted coronary heart disease risk in rural Chinese adults with an area under the curve of 0.9711 on the test set, outperforming SVM and LASSO models.
Why the study?
Coronary heart disease has high morbidity and mortality in China, and risk prediction models offer value for early prevention and diagnosis.
Does a Random Forest algorithm improve the prediction of coronary heart disease compared to SVM and LASSO in rural Chinese adults?
Cross-Sectional
Does a Random Forest algorithm improve the prediction of coronary heart disease compared to SVM and LASSO in rural Chinese adults?
A Random Forest algorithm provides highly accurate prediction of coronary heart disease risk in rural Chinese adults, outperforming SVM and LASSO models.
May support ML-based CHD risk prediction in rural Chinese adults; leaves open prospective validation before clinical use.
BACKGROUND: Coronary heart disease (CHD) is a common cardiovascular disease with high morbidity and mortality in China. The CHD risk prediction model has a great value in early prevention and diagnosis. METHODS: In this study, CHD risk prediction models among rural residents in Xinxiang County were constructed using Random Forest (RF), Support Vector Machine (SVM), and the least absolute shrinkage and selection operator (LASSO) regression algorithms with identified 16 influencing factors. RESULTS: Results demonstrated that the CHD model using the RF classifier performed best both on the training set and test set, with the highest area under the curve (AUC = 1 and 0.9711), accuracy (one and 0.9389), sensitivity (one and 0.8725), specificity (one and 0.9771), precision (one and 0.9563), F1-score (one and 0.9125), and Matthews correlation coefficient (MCC = one and 0.8678), followed by the SVM (AUC = 0.9860 and 0.9589) and the LASSO classifier (AUC = 0.9733 and 0.9587). Besides, the RF model also had an increase in the net reclassification index (NRI) and integrated discrimination improvement (IDI) values, and achieved a greater net benefit in the decision curve analysis (DCA) compared with the SVM and LASSO models. CONCLUSION: The CHD risk prediction model constructed by the RF algorithm in this study is conducive to the early diagnosis of CHD in rural residents of Xinxiang County, Henan Province.
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Wang et al. (2021) conducted a cross-sectional in Coronary heart disease. Random Forest (RF) algorithm vs. Support Vector Machine (SVM) and LASSO regression algorithms was evaluated on Model performance (AUC on test set). A Random Forest algorithm predicted coronary heart disease risk in rural Chinese adults with an area under the curve of 0.9711 on the test set, outperforming SVM and LASSO models.
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