Explainable machine learning-based mortality risk stratification for older adults with COVID-19: pinpointing core immunological biomarkers and revealing dose-threshold effects

BackgroundOlder adults are the high-risk group for COVID-19-related death. This study aimed to develop an accurate, efficient, clinically interpretable machine learning (ML) model for predicting mortality risk in this population, using only routine hematological indicators at admission to avoid extra medical costs and radiation exposure.Methods2393 COVID-19 patients were enrolled in this retrospective study.

Missing values were imputed via Random Forest. RandomOverSampler was utilized during model training to alleviate moderate class imbalance.

Feature selection was conducted following the maximum relevance-minimum redundancy principle. Five ML algorithms—Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), XGBoost (XGB), Light Gradient Boosting Machine (LGBM) were optimized via bayesian optimization (BO).

We performed 10 rounds of random stratified data splitting; models were fitted on the training set, with intermediate screening and hyperparameter optimization implemented on the validation set. The independent held-out test set was strictly reserved for final performance evaluation.

Model performances were assessed using the ROC curve, accuracy, precision, recall, F1-score and brier score.