Designing an explainable algorithm based on XGBoost and genetic algorithm for predicting hospitalization needs of COVID-19 patients.

Timely identification of COVID-19 outpatients who are at risk of hospitalization is critical for preventing clinical deterioration and optimizing healthcare resources. Although machine-learning models have demonstrated high predictive accuracy, their limited interpretability often hinders clinical adoption. This study aims to develop a hybrid explainable framework that combines the predictive strength of XGBoost with clinically interpretable rule-based explanations to support decision-making in real clinical settings. A retrospective dataset of 1278 COVID-19 patients was analyzed after applying strict inclusion and exclusion criteria. Twenty-seven clinical, laboratory, and demographic variables were preprocessed using outlier detection, multiple imputation by chained equations, and stratified train-test splitting validated through a Kolmogorov-Smirnov test. XGBoost was trained and benchmarked against logistic regression, random forest, LightGBM, and a neural network. For interpretability, candidate rules were extracted from a constrained Random Forest and optimized via a genetic algorithm (GA) using accuracy-support multi-objective fitness. Clinical validation of rules was performed by ten physicians using the Content Validity Index (CVI; threshold ≥ 0.85). XGBoost achieved superior predictive performance with an AUC of 0.85, sensitivity of 73.5%, specificity of 88.7%, AUPRC of 0.72, and a Brier Score of 0.085. Baseline models demonstrated lower discrimination and calibration. Fairness evaluation indicated stable model behavior across demographic and comorbidity subgroups. Sensitivity analysis identified SpO