Interpretable Machine Learning for Mortality Risk Stratification in Hantavirus Infection: A Global Study Across Hemorrhagic Fever with Renal Syndrome and Hantavirus Cardiopulmonary Syndrome

Abstract

Hantavirus infection remains a rare but potentially fatal zoonosis, and early identification of patients at highest risk of death is essential for timely triage and resource allocation. This study developed an interpretable machine learning (ML) framework for mortality risk stratification using a global hantavirus epidemiology dataset. The dataset included both Hemorrhagic Fever with Renal Syndrome (HFRS) and Hantavirus Cardiopulmonary Syndrome (HCPS) cases. The study conducted a retrospective supervised learning analysis on 10,000 patient records, including demographic, clinical, epidemiological, and treatment variables. The binary outcome was mortality. Preprocessing included identifier removal, missing-value handling, categorical and symptom encoding, feature selection, and class-imbalance correction. Logistic regression, random forest, and extreme gradient boosting (XGBoost) models were trained and compared on a held-out test set using receiver operating characteristic-area under the curve (ROC-AUC), accuracy, precision, recall, F1-score, and Brier score. Model interpretation was planned using SHapley Additive exPlanations-based feature attribution. The cohort included 8,938 recovered and 1,062 deceased cases (mortality rate: 10.62%), comprising 6,460 HFRS cases (64.6%) and 3,540 HCPS cases (35.4%). Logistic regression achieved the highest discrimination, with an ROC-AUC of 0.858 and the highest recall for mortality detection (0.741), but modest precision (0.321) and weaker calibration (Brier score 0.119). Gradient boosting showed comparable discrimination (AUC 0.857) with better precision (0.511) and calibration (Brier score 0.076). Random forest performed less well for mortality detection, with markedly low recall (0.085). The study trained and compared interpretable classifiers using 10,000 global hantavirus cases representing both HFRS (64.6%) and HCPS (35.4%) presentations, and reported pooled performance as well as syndrome-stratified results. Severity, syndrome type, viral load category, age, and geographic setting were the most informative predictors. Mortality risk in hantavirus infection can be modeled using routine clinical and epidemiological features, but clinical deployment should prioritize sensitivity, calibration, and transparency over accuracy alone. These findings support the use of interpretable ML as a practical framework for early hantavirus risk stratification and external validation in independent cohorts.