Development of explainable artificial intelligence based machine learning model for predicting 30-day hospital readmission after renal transplantation - Nasser Alnazari; Omar Ibrahim Alanazi; Muath Owaidh Alosaimi; Ziyad Mohamed Alanazi; Ziyad Mohammed Alhajeri; Khaled Mohammed Alhussaini; Abdulkarim Mekhlif Alanazi; Ahmed Y. Azzam (BMC Nephrology)
Abstract Background Hospital readmission following renal transplantation significantly impacts patient outcomes and healthcare resources. While machine learning approaches offer promising solutions for risk prediction, their clinical application often lacks interpretability. We developed an explainable artificial intelligence (XAI) based supervised learning model to predict 30-day hospital readmission risk following renal transplantation. Methods We conducted a retrospective analysis of 588 renal transplant recipients at King Abdullah International Medical Research Center, with a predominance of living donor transplants (85.2%, n = 500). Our methodology included a four-stage machine learning pipeline: data processing, feature preparation, model development using stratified 5-fold cross-validation, and clinical validation. Multiple algorithms were evaluated, with gradient boosting demonstrating superior performance. Model interpretability was achieved through dual-approach analysis using SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations). Results The gradient boosting model demonstrated strong performance (AUC 0.837, 95% CI: 0.802–0.872) with accuracy of 0.796 ± 0.050 and sensitivity of 0.388 ± 0.129. Length of hospital stay (38.0% contribution) and post-transplant systolic blood pressure (30.0% contribution) emerged as primary predictors, with differences between living and deceased donor subgroups. Pre-transplant BMI showed a higher importance in deceased donor recipients (12.6% vs. 2.6%), while HbA1c and eGFR were more impacting in living donor outcomes. The readmission rate in our cohort (88.9%, n = 523) was higher than previously reported ranges (18–47%), likely reflecting center-specific practices. Conclusions Our XAI-based machine learning model combines strong predictive performance with clinical interpretability, offering transplant physicians donor-specific risk stratification capabilities. The web-based implementation facilitates practical integration into clinical workflows. Given our single-center experience and high proportion of living donors, external validation across diverse transplant centers is essential before widespread implementation. Our approach establishes a framework for developing center-specific risk prediction tools in transplant medicine.