Predictive Machine Learning Framework for Medical Relief Supply Decision-Making in Disaster Response Operations
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This study aims to strengthen decision-making in medical relief supply management through computational modeling and machine learning. Specifically, it seeks to develop a predictive model that supports effective medical supply allocation during disaster response operations. By experimenting with multiple algorithms and optimization techniques, the study endeavors to identify the most suitable machine learning approach for accurate and real-time prediction of supply reorder needs. The study adopts the SEMMA methodology as the framework for developing decision-making models that focus on optimizing medical supply distribution during natural calamities. Both ensemble and classical algorithms were implemented to compare predictive performance. Ensemble methods included Random Forest, Gradient Boosting, HistGradientBoosting, AdaBoost, XGBoost, LightGBM, and CatBoost, while classical algorithms comprised of Decision Tree, Logistic Regression, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). Model evaluation utilized the F1-score and ROC-AUC metrics, with hyperparameter tuning conducted via Randomized Search and 5-fold cross-validation to ensure robust generalization. Results revealed that CatBoost achieved the highest F1-score (0.9316), outperforming other models such as XGBoost (0.9282), Gradient Boosting (0.9279), and LightGBM (0.9277). These findings underscore the superiority of gradient boosting ensemble methods in capturing complex relationships and improving prediction accuracy. Traditional models, including AdaBoost, Decision Tree, Logistic Regression, KNN, and SVM are yielded with lower F1-scores ranging from 0.8051 to 0.8738 only and indicating its reduced predictive capability after optimization. The CatBoost algorithm demonstrated the most reliable and accurate predictive performance by highlighting its potential as a robust decision- support tool for optimizing medical supply allocation during disaster response operations.
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