Game Theory Decision Model for Differential Diagnosis of Epilepsy Symptomatic Patients

Epilepsy misdiagnosis is a prevalent issue with significant clinical, psychological, and economic consequences. Existing diagnostic approaches, such as electroencephalography and neuroimaging, provide valuable insights into neurological activity and structural abnormalities. However, these methods have limitations in distinguishing epilepsy from psychogenic nonepileptic seizures and other conditions with similar presentations, often leading to diagnostic uncertainty or misclassification. This study introduces a novel game-theoretic framework to enhance diagnostic accuracy by modeling the physician-patient interaction as a sequential decision-making process. The model considers the strategic choices of physicians and patients, incorporating psychological factors to refine differential diagnosis. A hybrid payoff assignment method, integrating binary and multi-criteria utility approaches, ensures a realistic representation of medical decision-making. Equilibrium analysis, conducted across four psychological distress categories, identifies optimal diagnostic strategies based on patient adherence and physician recommendations. The results illustrate that while distress levels influence treatment direction where low and moderate distress favor continued neurological care and high distress suggests psychiatric referral, this decision-making process is not straightforward. The game-theoretic model formalizes the strategic interaction between physicians and patients, incorporating the probabilities of diagnostic accuracy, treatment adherence, and long-term patient outcomes into the decision framework. The payoff formulation accounts for both medical and psychological factors, ensuring that referrals are not solely based on distress levels but on an optimized diagnostic pathway that balances competing risks and benefits. This structured approach helps mitigate misdiagnosis and inappropriate treatment allocation, which simple distress scoring alone cannot achieve. These findings underscore the importance of integrating psychological assessments into diagnostic protocols to reduce misdiagnosis. The proposed framework provides a structured decision-support tool that improves diagnostic precision, optimizes resource allocation, and enhances patient outcomes. Future research should explore empirical validation, machine learning integration, and cross-cultural applicability to further refine this approach.