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• W4385429690 abstract "Abstract Large-scale screening for the risk of coronary heart disease (CHD) is crucial for its prevention and management. Physical examination data has the advantages of wide coverage, large capacity, and easy collection. Therefore, here we report a gender-specific cascading system for risk assessment of CHD based on physical examination data. The dataset consists of 39,538 CHD patients and 640,465 healthy individuals from the Luzhou Health Commission in Sichuan, China. Fifty physical examination characteristics were considered, and after feature screening, ten risk factors were identified. To facilitate large-scale CHD risk screening, a CHD risk model was developed using a fully connected network (FCN). For males, the model achieves AUCs of 0.8671 and 0.8659, respectively on the independent test set and the external validation set. For females, the AUCs of the model are 0.8991 and 0.9006, respectively on the independent test set and the external validation set. Furthermore, to enhance the convenience and flexibility of the model in clinical and real-life scenarios, we established a CHD risk scorecard base on logistic regression (LR). The results show that, for both males and females, the AUCs of the scorecard on the independent test set and the external verification set are only slightly lower (<0.05) than those of the corresponding prediction model, indicating that the scorecard construction does not result in a significant loss of information. To promote CHD personal lifestyle management, an online CHD risk assessment system has been established, which can be freely accessed at http://lin-group.cn/server/CHD/index.html ." @default.
• W4385429690 created "2023-08-01" @default.
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• W4385429690 date "2023-07-31" @default.
• W4385429690 modified "2023-10-16" @default.
• W4385429690 title "A gender specific risk assessment of coronary heart disease based on physical examination data" @default.
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• W4385429690 doi "https://doi.org/10.1038/s41746-023-00887-8" @default.
• W4385429690 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37524859" @default.
• W4385429690 hasPublicationYear "2023" @default.
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