Explainable AI-Driven Clinical Decision Support for Mortality Risk Stratification in Pediatric Respiratory Disorders

Abstract

Nowadays, respiratory illnesses are common among children around the world, which is contributing significantly to child mortality due to rapid growth. These diseases appear with several symptoms, and several may share similar symptoms; the most well-known include asthma, bronchiolitis, and pneumonia. The mortality rate associated with chronic pediatric respiratory conditions is increasing annually, underscoring the need to assess the severity of these diseases. This research aims to improve the mortality prediction in pediatric respiratory disorders by using a robust machine learning architecture and a comprehensive dataset from the Children’s Hospital of Rabat, Morocco. Ten machine learning algorithms were implemented after thorough preprocessing, which included encoding, feature selection, oversampling, and hyperparameter tuning. Random Forest, Logistic Regression, AdaBoost, Multilayer Perceptron, K-Nearest Neighbors, Decision Trees, CatBoost, XGBoost, Naïve Bayes, and Gradient Boosting have been applied to get better results. With an accuracy rate of 99.29%, a precision of 92.68%, a recall rate of 92.55%, and an F1-score of 92.58%, Random Forest performed more effectively than the other methods. Explainable artificial intelligence techniques, such as Shapley Additive Values and Local Interpretable Model-agnostic Explanations, ensure clinician trust and model interpretability. 

Received: 22 November 2025 | Revised: 3 April 2026 | Accepted: 3 June 2026

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The data that support the findings of this study are openly available in the Mendeley Data repository at https://data.mendeley.com/datasets/2d9dvnycjw/1.

Author Contribution Statement

Khandaker Mohammad Mohi Uddin: Conceptualization, Methodology, Writing – review & editing, Supervision. Anjuman Ansary: Software, Validation, Formal analysis, Investigation, Writing – original draft. Nazim Uddin: Visualization, Project administration. Md. Tofael Ahmed Bhuiyan: Resources, Data curation.