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Machine learning algorithms for predicting mortality after coronary artery bypass grafting
Amirmohammad Khalaji1,2,3, Amir Hossein Behnoush1,2,3, Mana Jameie1,3,4
1Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.
Insights
Machine learning models can predict mortality after coronary artery bypass grafting (CABG). Logistic Regression (LR) showed the highest predictive accuracy, aiding clinical decisions for high-risk patients.
Area of Science:
- Cardiovascular Surgery
- Medical Informatics
- Machine Learning in Healthcare
Background:
- Big data analytics is increasingly utilized in healthcare.
- Machine learning (ML) offers potential for predicting clinical outcomes.
- Coronary artery bypass grafting (CABG) outcomes require accurate prediction models.
Purpose of the Study:
- To evaluate the predictive performance of various ML models for mortality after CABG.
- To identify key predictors of mortality in CABG patients.
- To compare the efficacy of different ML algorithms in predicting CABG mortality.
Main Methods:
- Utilized a CABG data registry with baseline and follow-up features.
- Selected key variables using the random forest method.
- Developed and assessed prediction models using Logistic Regression (LR), Support Vector Machine (SVM), Naïve Bayes (NB), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) algorithms, evaluating performance with Area Under the Curve (AUC).
Main Results:
- Included 16,850 patients undergoing isolated CABG; 468 deaths occurred within one year.
- Total ventilation hours and left ventricular ejection fraction were significant predictors of mortality.
- All ML models demonstrated acceptable performance (AUC > 0.7) for one-year mortality prediction.
- Logistic Regression (LR) achieved the highest AUC (0.811), followed closely by XGBoost (0.792).
- LR also showed the highest predictive ability for two-to-five-year mortality.
Conclusions:
- Multiple machine learning models exhibit acceptable performance in predicting CABG-related mortality.
- Logistic Regression (LR) demonstrated superior predictive capability compared to other evaluated ML models.
- These ML models can assist clinicians in risk stratification and decision-making for patients undergoing CABG.
Background:
As the era of big data analytics unfolds, machine learning (ML) might be a promising tool for predicting clinical outcomes. This study aimed to evaluate the predictive ability of ML models for estimating mortality after coronary artery bypass grafting (CABG).
Materials And Methods:
Various baseline and follow-up features were obtained from the CABG data registry, established in 2005 at Tehran Heart Center. After selecting key variables using the random forest method, prediction models were developed using: Logistic Regression (LR), Support Vector Machine (SVM), Naïve Bayes (NB), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) algorithms. Area Under the Curve (AUC) and other indices were used to assess the performance.
Results:
A total of 16,850 patients with isolated CABG (mean age: 67.34 ± 9.67 years) were included. Among them, 16,620 had one-year follow-up, from which 468 died. Eleven features were chosen to train the models. Total ventilation hours and left ventricular ejection fraction were by far the most predictive factors of mortality. All the models had AUC > 0.7 (acceptable performance) for 1-year mortality. Nonetheless, LR (AUC = 0.811) and XGBoost (AUC = 0.792) outperformed NB (AUC = 0.783), RF (AUC = 0.783), SVM (AUC = 0.738), and KNN (AUC = 0.715). The trend was similar for two-to-five-year mortality, with LR demonstrating the highest predictive ability.
Conclusion:
Various ML models showed acceptable performance for estimating CABG mortality, with LR illustrating the highest prediction performance. These models can help clinicians make decisions according to the risk of mortality in patients undergoing CABG.
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