Abstract
Integrating machine learning with conventional optimization techniques enhances the accuracy, interpretability, and clinical relevance of predictive analytics in healthcare. This study proposes a hybrid approach that combines machine learning’s predictive capabilities with optimization’s decision-making rigor to improve patient outcome predictions and resource allocation. By incorporating constraints and objectives directly into predictive models, this framework ensures that healthcare predictions are not only accurate but also actionable and aligned with clinical priorities. This approach is particularly valuable in applications such as patient readmission prediction and treatment planning, where balancing predictive accuracy with real-world constraints is essential. Machine learning models analyze complex healthcare data to forecast patient outcomes, while optimization techniques refine these predictions by considering feasibility factors such as treatment costs, resource availabilit