Table 1.
A summarized list of related research works.
Fig 1.
Different steps of the proposed methodology.
Table 2.
Main features of our dataset of cardiac surgery ICUs.
Fig 2.
Feature importance with Random Forest and Gradient Boosting.
Table 3.
The hyperparameters set for each ML and DL model.
Table 4.
Evaluation metrics of ML and DL models before hyperparameter tuning using SA algorithm.
Table 5.
ML and DL models with their specific hyperparameters’ settings.
Table 6.
Evaluation metrics of ML and DL models after hyperparameter tuning using SA algorithm.
Table 7.
Comparison of tuned ensemble using SA and GA with untuned ensemble and AutoML.
Fig 3.
Optimization plot of ML models using SA algorithm.
Fig 4.
Optimization plot of ML models using GA algorithm.
Fig 5.
Correlation analysis between features.
Fig 6.
Countplot of ’CABG_Valve’ and ’Cross_Clamp’ Based on ’On_Pump’.
Fig 7.
Boxplot of ’EF’ versus ’Grade of Age’ Stratified by ’Sex’.