What question did this study set out to answer?

This research aims to compare the performance of various machine learning algorithms for predicting mortality in intensive care using the MIMIC-III dataset.

February 28, 2026Open Access

Explainable AI for mortality prediction: a comparative study using the MIMIC-III dataset

Puntos clave

This research aims to compare the performance of various machine learning algorithms for predicting mortality in intensive care using the MIMIC-III dataset.
Analyzed a retrospective cohort of 600 patient records from the MIMIC-III dataset.
Evaluated algorithms including support vector machine, K-nearest neighbour, decision trees, gradient boosting, random forests, Naive Bayes, logistic regression, and extra trees.
Assessed model performance using metrics such as F1 score, sensitivity, and accuracy with threefold cross-validation.
Applied SHapley Additive exPlanations (SHAP) to identify key factors influencing mortality predictions.
Extra trees and gradient boosting achieved the highest accuracy at 98.33% and 98.23%, respectively.
Support vector machine also performed well with 97.50% accuracy and an F1 score of 94.34%.
Random forests and decision trees had accuracies of 95.00% and 94.17%, respectively.
Naive Bayes reached 96.67% accuracy with a perfect recall of 100%; KNN and logistic regression performed lower at 76.67% and 75.83%.
Key predictors identified by SHAP included hypertension, tumours, and endocrine and digestive diseases.

Resumen

Objectives Predicting mortality is vital for tailoring treatments, improving care and reducing costs. Machine learning (ML) has shown strong potential, often outperforming traditional severity-of-illness scoring systems in intensive care units (ICUs). However, the black-box nature of ML limits adoption. This study evaluates the accuracy of several ML algorithms on the Medical Information Mart for Intensive Care III (MIMIC-III) dataset and applies explainable artificial intelligence to identify variables influencing ICU mortality. Methods A retrospective cohort of 600 MIMIC-III patient records was analysed. ML algorithms tested included support vector machine (SVM), K-nearest neighbour (KNN), decision trees (DT), gradient boosting (GB), random forests (RF), Naive Bayes (NB), logistic regression (LR) and extra trees (ET). Models were assessed with threefold cross-validation using F1 Score, sensitivity, specificity, confusion matrix and accuracy. SHapley Additive exPlanations (SHAP) was applied to explain key factors influencing predictions. Results Among the eight algorithms evaluated, ET and GB achieved the highest accuracy (98.33% and 98.23%, respectively) with F1-scores above 96%. SVM also performed strongly (97.50% accuracy, F1=94.34%). RF and DT yielded accuracies of 95.00% and 94.17%, respectively. NB reached 96.67% accuracy with 100% recall, while KNN and LR showed lower discriminative performance (76.67% and 75.83% accuracy, respectively). SHAP, LIME and Shapley values consistently identified hypertension, tumours and endocrine and digestive disease as the leading predictors of mortality. Discussion Findings highlight ML’s potential to optimise ICU decision-making and support clinicians. However, reliance on retrospective data remains a limitation, and clinical validation is required. Conclusion ML algorithms are highly effective for mortality prediction, and explainability is key for trust and adoption. When combined, accuracy and interpretability enable ML to safely support informed ICU decisions and improve patient outcomes.

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