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

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...

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Autores principales: Wang, Qian, Li, Wenxing, Wang, Yongbin, Li, Huijun, Zhai, Desheng, Wu, Weidong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8515995/
https://www.ncbi.nlm.nih.gov/pubmed/34721974
http://dx.doi.org/10.7717/peerj.12259
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author Wang, Qian
Li, Wenxing
Wang, Yongbin
Li, Huijun
Zhai, Desheng
Wu, Weidong
author_facet Wang, Qian
Li, Wenxing
Wang, Yongbin
Li, Huijun
Zhai, Desheng
Wu, Weidong
author_sort Wang, Qian
collection PubMed
description 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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spelling pubmed-85159952021-10-28 Prediction of coronary heart disease in rural Chinese adults: a cross sectional study Wang, Qian Li, Wenxing Wang, Yongbin Li, Huijun Zhai, Desheng Wu, Weidong PeerJ Cardiology 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. PeerJ Inc. 2021-10-11 /pmc/articles/PMC8515995/ /pubmed/34721974 http://dx.doi.org/10.7717/peerj.12259 Text en © 2021 Wang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Cardiology
Wang, Qian
Li, Wenxing
Wang, Yongbin
Li, Huijun
Zhai, Desheng
Wu, Weidong
Prediction of coronary heart disease in rural Chinese adults: a cross sectional study
title Prediction of coronary heart disease in rural Chinese adults: a cross sectional study
title_full Prediction of coronary heart disease in rural Chinese adults: a cross sectional study
title_fullStr Prediction of coronary heart disease in rural Chinese adults: a cross sectional study
title_full_unstemmed Prediction of coronary heart disease in rural Chinese adults: a cross sectional study
title_short Prediction of coronary heart disease in rural Chinese adults: a cross sectional study
title_sort prediction of coronary heart disease in rural chinese adults: a cross sectional study
topic Cardiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8515995/
https://www.ncbi.nlm.nih.gov/pubmed/34721974
http://dx.doi.org/10.7717/peerj.12259
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