Hospital readmission within 30 days remains a significant challenge in oncology practice, contributing to higher healthcare costs, treatment delays, and poorer patient outcomes. Existing predictive models for breast cancer readmission are often limited by inadequate interpretability and generalisability. This study develops and evaluates an explainable machine learning (ML) framework to predict 30-day hospital readmissions among breast cancer patients, with specific emphasis on methodological transparency and avoidance of information leakage. A retrospective dataset including demographic, clinical, and treatment-related variables such as age, comorbidity burden, ECOG performance status, baseline neutrophil count, and dosage adjustments was analysed. Multiple ML classifiers were evaluated—including Logistic Regression, Support Vector Machine, Naïve Bayes, K-Nearest Neighbours, Decision Tree, Random Forest, and XGBoost—using repeated stratified cross-validation (5 × 10 folds). Class imbalance was addressed using SMOTE applied strictly within the training folds to prevent data leakage. Out-of-fold performance metrics included ROC-AUC, PR-AUC, calibration curves, and Brier scores. Random Forest demonstrated the strongest discrimination specificity of 0.57 ± 0.33, the highest among all models, and a superior ROC-AUC of 0.68 ± 0.17, which was appropriate for the small, imbalanced dataset. For interpretability, each model was refit on the full dataset and analysed using Shapley Additive Explanations (SHAP), Partial Dependence Plots (PDP), and LIME. Comorbidity burden and ECOG performance status consistently emerged as the most influential predictors across all explainability techniques, aligning with established clinical evidence. The findings highlight the feasibility of applying explainable ML methods to small, imbalanced oncology datasets and demonstrate their potential to support early clinical risk identification in breast cancer care.
Mqadi et al. (Wed,) studied this question.