Explainable machine learning for predicting clinical outcomes in HIV/TB co-infection: a comparative retrospective study

Abstract Background HIV/TB co-infection presents substantial public-health challenges, showing greater treatment-failure and mortality rates than tuberculosis alone. Recent advances in machine learning (ML) provide a robust means of identifying high-risk patients early in the disease course. Methods This retrospective study enrolled 359 patients co-infected with HIV and TB at a single tertiary-care hospital. We extracted clinical and immunological data. The cohort was subsequently divided into training (0%) and test (0%) subsets, and class imbalance was addressed with the Synthetic Minority Over-sampling Technique (SMOTE). Six ML classifiers—Random Forest, XGBoost, LightGBM, Support Vector Machine, Extra Trees and CatBoost—were trained after grid-search hyper-parameter tuning. Model performance was assessed with the area under the receiver-operating-characteristic curve (AUC), accuracy, recall, precision, specificity and F1-score. Multi-criteria ranking was then conducted with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The leading model was interpreted using SHapley Additive exPlanations (SHAP). Results Overall, 304 of 359 patients (84.7%) had favourable outcomes, whereas 55 (15.3%) had unfavourable outcomes. LightGBM achieved the best overall performance (AUC = 0.771; accuracy = 84.72%; F1 = 0.522) and was ranked first by TOPSIS. SHAP analysis highlighted age, CD4 and CD8 counts, body-mass index and occupation as key predictors. Lower BMI, pronounced immunosuppression and older age were strongly associated with unfavourable outcomes, findings that align with established clinical evidence. Conclusion A gradient-boosted model (LightGBM) combined with SHAP interpretation demonstrated reliable predictive performance in HIV/TB co-infection and highlighted clinically actionable risk factors. Incorporating this tool into routine workflows could enable healthcare providers to identify high-risk individuals earlier, allocate resources more efficiently and, ultimately, improve TB-treatment success. Clinical trial registration Not applicable.