Machine learning models with SHAP values provide highly accurate predictions of coronary artery disease risk and identify individual modifiable risk factors to enhance primary prevention.
Can machine learning models with SHAP values accurately predict coronary artery disease risk and identify modifiable risk factors in patients in Xinjiang, China?
Machine learning models using SHAP values offer interpretable, personalized predictions of CAD risk to enhance primary prevention.
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Machine learning models can provide personalized and highly accurate predictions of CAD risk. The interpretability of these models facilitates the identification of modifiable risk factors in individual patients, offering valuable insights to enhance primary prevention and management of cardiovascular disease in the Xinjiang region.
Maimaitituersun et al. (Sat,) reported a other. Machine learning models with SHAP values provide highly accurate predictions of coronary artery disease risk and identify individual modifiable risk factors to enhance primary prevention.