Evaluating the impact of metabolic indicators and scores on cardiovascular events using machine learning

Figures

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  Fig. 1. Machine learning model evaluation of various metabolic indicators and scores in the angina pectoris population. A AUC results graph of the training set. B AUC results graph of the testing set. C Forest plot of AUC scores. D Summary of the shap model of the XGBoost algorithm. E Importance ranking results of the shap model of the XGBoost algorithm

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  Fig. 2. Machine learning model evaluation of various metabolic indicators and scores in the coronary disease population. A AUC results graph of the training set. B AUC results graph of the testing set. C Forest plot of AUC scores. D Summary of the shap model of the XGBoost algorithm. E Importance ranking results of the shap model of the XGBoost algorithm

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  Fig. 3. Machine learning model evaluation of various metabolic indicators and scores in the myocardial infarction population. A AUC results graph of the training set. B AUC results graph of the testing set. C Forest plot of AUC scores. D Summary of the shap model of the XGBoost algorithm. E Importance ranking results of the shap model of the XGBoost algorithm

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  Fig. 4. Machine learning model evaluation of various metabolic indicators and scores in the heart failure population. A AUC results graph of the training set. B AUC results graph of the testing set. C Forest plot of AUC scores. D Summary of the shap model of the XGBoost algorithm. E Importance ranking results of the shap model of the XGBoost algorithm

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  Fig. 5. Evaluation of AUC values for AP, CHD, HF, MI prediction using 100 machine learning algorithms. A presents the AUC values for AP prediction using 100 machine learning algorithms. B shows the AUC values for CHD prediction using 100 machine learning algorithms. C displays the AUC value of SVM + GBM algorithm on the training set. D illustrates the AUC value of SVM + GBM algorithm on the testing set. E demonstrates the AUC value of Enet[alpha = 0.3] + GBM algorithm on the training set. F depicts the AUC value of Enet[alpha = 0.3] + GBM algorithm on the testing set. G exhibits the AUC value of Enet[alpha = 0.8] + GBM algorithm on the training set. H showcases the AUC value of Enet[alpha = 0.8] + GBM algorithm on the testing set. I shows the AUC value of GBM algorithm on the training set. J reveals the AUC value of GBM algorithm on the testing set. K represents the AUC value for HF prediction using 100 machine learning algorithms. L indicates the AUC value for MI prediction using 100 machine learning algorithms