Explainable machine and deep learning framework for newborn health monitoring: A simulation-based approach

Abstract

Machine learning and deep learning techniques are increasingly being adopted in neonatal health research to improve early risk detection and support clinical decision-making. Progress, however, is limited by the scarcity and ethical constraints of real-world neonatal datasets, which restrict model development and validation. To address this, we employed a synthetic but medically realistic dataset simulating daily health records of newborns to evaluate the performance of four classification models: k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and TabTransformer. Models were assessed using accuracy, precision, recall, F1-score, Cohen’s Kappa, confusion matrices, and SHAP-based explainability analyses. Results indicated that the TabTransformer consistently outperformed the other models, achieving the highest test accuracy (96.5 %) and demonstrating superior recall for the minority at-risk class, highlighting its ability to capture complex neonatal health patterns. MLP and SVM delivered competitive results, whereas kNN, while interpretable, showed reduced generalization under class imbalance. These findings demonstrate the promise of transformer based architectures for neonatal health monitoring and emphasize the need to validate such models using real-world clinical datasets in future studies.