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Designing an explainable algorithm based on XGBoost and genetic algorithm for predicting hospitalization needs of
Azadeh Abkar1, Mahdi Mehrabi2, Amin Golabpour3
1Department of Computer Engineering, Shi.C., Islamic Azad University, Shiraz, Iran.
Scientific Reports
|February 23, 2026
Summary
This study developed an explainable AI framework to predict COVID-19 hospitalization risk. The hybrid system combines XGBoost predictions with interpretable rules, improving clinical decision-making for outpatients.
Area of Science:
- Artificial Intelligence in Medicine
- Clinical Decision Support Systems
- Epidemiology and Public Health
Background:
- Accurate prediction of COVID-19 hospitalization risk for outpatients is crucial for resource allocation and patient care.
- Existing machine learning models often lack interpretability, limiting their clinical adoption.
- There is a need for transparent and reliable tools to support clinical decision-making in managing COVID-19 patients.
Purpose of the Study:
- To develop a hybrid explainable artificial intelligence (XAI) framework integrating XGBoost and rule-based explanations.
- To enhance the interpretability of machine learning models for COVID-19 hospitalization risk prediction.
- To provide a clinically validated decision-support tool for assessing hospitalization risk in COVID-19 outpatients.
Main Methods:
- A retrospective analysis of 1278 COVID-19 outpatients using 27 clinical, laboratory, and demographic variables.
- Development of a hybrid framework combining XGBoost for prediction and a genetic algorithm-optimized Random Forest for rule extraction.
- Clinical validation of extracted rules by ten physicians using the Content Validity Index (CVI).
Main Results:
- XGBoost achieved superior predictive performance (AUC 0.85) compared to baseline models.
- The framework generated 40 clinically valid, interpretable IF-THEN rules, validated by physicians (CVI ≥ 0.85).
- Key predictors identified include SpO2, CRP, age, D-dimer, ferritin, and lymphocyte percentage.
Conclusions:
- The hybrid XAI framework successfully integrates high-accuracy predictions with clinically interpretable rules for COVID-19 hospitalization risk assessment.
- This transparent decision-support tool can improve triage and resource optimization for COVID-19 outpatients.
- The modular design allows for adaptation to future infectious disease outbreaks, enhancing clinical deployment.
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