Comparison of the proposed hybrid model with state of the art machine learning models on MIMIC-III test set. Performance metrics are reported as point estimates with 95% confidence intervals in parentheses. The proposed SVM-PSO- AI framework is compared to Logistic regression, K-Nearest Neighbors (KNN), a Decision Tree, which used Naive Bayes ensemble, and a Boosted Ensemble model. The comparison will be made using the standard classification metrics: Accuracy, Precision, Recall, F1-Score and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The findings illustrate that the proposed hybrid model has the best predictive capability in all the measures considered.

Comparison of the proposed hybrid model with state of the art machine learning models on MIMIC-III test set. Performance metrics are reported as point estimates with 95% confidence intervals in parentheses. The proposed SVM-PSO- AI framework is compared to Logistic regression, K-Nearest Neighbors (KNN), a Decision Tree, which used Naive Bayes ensemble, and a Boosted Ensemble model. The comparison will be made using the standard classification metrics: Accuracy, Precision, Recall, F1-Score and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The findings illustrate that the proposed hybrid model has the best predictive capability in all the measures considered.