Prediction of coronary heart disease in rural Chinese adults: a cross sectional study.

ABSTRACT:

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 (on

SUBMITTER: Wang Q 

PROVIDER: S-EPMC8515995 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature