Researchers from School of Computer Science Provide Details of New Studies and Findings in the Area of Obsessive-Compulsive Disorder (An Iot-enabled Cyber-physical System for Early Detection of Obsessive-compulsive Disorder Using Neurosymbolic...).

The article focuses on research from Sambalpur, India, regarding an IoT-enabled Cyber-Physical System (H-CPS) designed for the early detection and classification of Obsessive-Compulsive Disorder (OCD). The system utilizes a hybrid machine learning framework that integrates Radial Basis Function Neural Networks, Competitive Neural Networks, and Neuro-Symbolic AI to enhance healthcare coordination. It employs Oxidative Stress Biomarkers as predictive indicators to differentiate between patients with OCD, genetically predisposed relatives, and healthy controls, achieving high prediction accuracies in experimental evaluations. The research demonstrates real-time feasibility for healthcare deployment with minimal latency, indicating its potential for practical applications. [Extracted from the article]