Prediction of all-cause mortality in coronary artery disease patients with atrial fibrillation based on machine learning models
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Xinyun Liu1,2,3, Jicheng Jiang4, Lili Wei2
1Soochow University, Suzhou, 215006, Jiangsu, People's Republic of China.
Insights
Machine learning models can predict all-cause mortality in coronary artery disease (CAD) patients with atrial fibrillation (AF). Regularization logistic regression showed the best performance, making it suitable for clinical practice.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Coronary artery disease (CAD) and atrial fibrillation (AF) are significant comorbidities.
- Predicting all-cause mortality in this patient group is crucial for clinical management.
Purpose of the Study:
- To develop and compare machine learning (ML) models for predicting all-cause mortality in CAD patients with AF.
- To identify the most effective ML model for clinical application.
Main Methods:
- Utilized data from 2037 CAD patients with AF.
- Developed three ML models: regularization logistic regression, random forest, and support vector machines.
- Evaluated model performance using fivefold cross-validation, calculating Area Under the Curve (AUC), sensitivity, specificity, and accuracy.
Main Results:
- Regularization logistic regression achieved the highest AUC (0.732), specificity (0.699), and accuracy (0.936).
- Random forest (AUC 0.728) and support vector machines (AUC 0.712) showed comparable performance.
- No statistically significant differences were found in the receiver operating characteristic (ROC) curves among the models.
Conclusions:
- Regularization logistic regression demonstrated superior performance in predicting all-cause mortality for CAD patients with AF.
- This model is recommended for implementation in clinical practice due to its predictive accuracy and specificity.
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.
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