Advancing Blood Supply Chain Prediction Based on a Novel Hybrid Machine Learning

Blood supply chains constitute a critical yet often overlooked component of modern public health systems, as they coordinate donors, collection centers, hospitals, and patients. One of the major operational challenges lies in planning the deployment of mobile blood collection units under highly variable and uncertain spatio-temporal demand. In this context, this study proposes a novel hybrid machine learning framework for predicting donor return potential and supporting location and time selection decisions for mobile blood drives. The proposed approach combines Support Vector Regression (SVR) and Light Gradient Boosting Machine (LGBM) through a dynamic, context-aware weighting function designed to capture both temporal regularities and nonlinear spatial heterogeneity in donor behavior. The model is evaluated using real-world data collected from a blood collection center operating multiple mobile units. Experimental results demonstrate that the proposed hybrid framework consistently outperforms its individual components, achieving R² values of up to 83% for certain locations, together with low Mean Absolute Error (MAE) and Mean Squared Error (MSE). These results confirm the robustness and stability of the proposed approach. Beyond predictive performance, the model is intended to be integrated into a decision-support system to help managers optimize logistical resources and improve the strategic planning of mobile blood collection campaigns. This work contributes to the emerging field of data-driven blood supply chain optimization by introducing a spatio-temporal, hybrid predictive core specifically designed for operational decision support.