Optimizing Resource Allocation in Healthcare Systems for Efficient Pandemic Management using Machine Learning and Artificial Neural Networks

Pandemics pose significant challenges to healthcare systems, particularly in resource allocation and patient care. Traditional methods for resource allocation often face limitations in handling complex data, adapting to changing conditions, and providing real-time decision support. To address these challenges, we propose a novel method for optimizing resource allocation in healthcare systems using machine learning (ML) and artificial neural networks (ANN s). Traditional resource allocation methods, often based on historical data and manual decision-making, may not be adequate to handle the dynamic and complex nature of pandemics. These methods often fail to account for real-time changes in disease patterns, patient needs, and resource availability. The proposed method integrates predictive modeling, optimization algorithms, and real-time decision support to enhance resource utilization and improve patient outcomes during pandemics.