The regularization logistic regression model predicted all-cause mortality with an AUC of 0.732, showing no significant difference compared to random forest and support vector machine models.
Cohort (n=2,037)
No
A regularization logistic regression model can effectively predict all-cause mortality in patients with concurrent coronary artery disease and atrial fibrillation, achieving an AUC of 0.732.
Effect estimate: AUC 0.732 (95% CI 0.649-0.816)
Absolute Event Rate: 0.732% vs 0.728%
p-value: p=>0.05
BACKGROUND: Machine learning (ML) can include more diverse and more complex variables to construct models. This study aimed to develop models based on ML methods to predict the all-cause mortality in coronary artery disease (CAD) patients with atrial fibrillation (AF). METHODS: A total of 2037 CAD patients with AF were included in this study. Three ML methods were used, including the regularization logistic regression, random forest, and support vector machines. The fivefold cross-validation was used to evaluate model performance. The performance was quantified by calculating the area under the curve (AUC) with 95% confidence intervals (CI), sensitivity, specificity, and accuracy. RESULTS: After univariate analysis, 24 variables with statistical differences were included into the models. The AUC of regularization logistic regression model, random forest model, and support vector machines model was 0.732 (95% CI 0.649-0.816), 0.728 (95% CI 0.642-0.813), and 0.712 (95% CI 0.630-0.794), respectively. The regularization logistic regression model presented the highest AUC value (0.732 vs 0.728 vs 0.712), specificity (0.699 vs 0.663 vs 0.668), and accuracy (0.936 vs 0.935 vs 0.935) among the three models. However, no statistical differences were observed in the receiver operating characteristic (ROC) curve of the three models (all P > 0.05). CONCLUSION: Combining the performance of all aspects of the models, the regularization logistic regression model was recommended to be used in clinical practice.
Liu et al. (Sat,) conducted a cohort in Coronary artery disease with atrial fibrillation (n=2,037). Regularization logistic regression model vs. Random forest and support vector machines models was evaluated on Prediction of all-cause mortality (Area Under the Curve) (AUC 0.732, 95% CI 0.649-0.816, p=>0.05). The regularization logistic regression model predicted all-cause mortality with an AUC of 0.732, showing no significant difference compared to random forest and support vector machine models.