Analysis of Behavioral Patterns of Prisoners of War Based on Medical Indicators Using a Hybrid Genetic Algorithm and Particle Swarm Optimization

This study focuses on analyzing behavioral patterns of prisoners of war (POWs) based on medical indicators, including stress biomarkers, neurophysiological data, and psychological assessments. A hybrid algorithm combining a genetic algorithm (GA) and particle swarm optimization (PSO) is proposed to identify complex behavioral patterns correlated with mental states and adaptive strategies of POWs. The method optimizes the processing of large datasets, accounting for nonlinear relationships between medical indicators and behavior. The results demonstrate the potential of the hybrid approach for predicting psychological risks and developing rehabilitation strategies. The study highlights the importance of an interdisciplinary approach to mental health analysis in traumatic contexts and offers new perspectives for applying artificial intelligence in military psychology.