التفاصيل البيبلوغرافية
| العنوان: |
Prediction of coronary heart disease in rural Chinese adults: a cross sectional study. |
| المؤلفون: |
Qian Wang, Wenxing Li, Yongbin Wang, Huijun Li, Desheng Zhai, Weidong Wu |
| المصدر: |
PeerJ; Oct2021, p1-20, 20p |
| مصطلحات موضوعية: |
CORONARY disease, SUPPORT vector machines, DECISION making, RANDOM forest algorithms, PREDICTION models |
| مصطلحات جغرافية: |
HENAN Sheng (China), CHINA |
| مستخلص: |
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. [ABSTRACT FROM AUTHOR] |
|
Copyright of PeerJ is the property of PeerJ Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
Full Text Finder