An IoT-Enabled Cyber-Physical System for Early Detection of Obsessive-Compulsive Disorder Using Neurosymbolic AI and Hybrid RBF-Competitive Neural Networks

Smart healthcare is advancing through the integration of Cyber-Physical Systems (CPS), Internet of Things (IoT), and Artificial Intelligence (AI). This work presents an IoT-enabled Healthcare CPS (H-CPS) for early detection and classification of Obsessive-Compulsive Disorder (OCD). The proposed system employs a hybrid machine learning framework that combines Radial Basis Function (RBF) Neural Networks, Competitive Neural Networks, and Neuro-Symbolic AI within a CPS architecture to enable intelligent coordination across healthcare infrastructure. The H-CPS leverages IoT devices, LoRa-based communication, and cloud computing to implement an OCD detection system. Oxidative Stress Biomarkers (OSBs) serve as predictive indicators, distinguishing patients with OCD (POCD), genetically predisposed first-degree relatives (GFDR), and healthy controls (HC). The model comprises a competitive input layer, an RBF-based nonlinear transformation layer, and a competitive clustering layer, with Neuro-Symbolic AI applied for cluster labeling. Experimental evaluation on two independent datasets achieved high prediction accuracies (0.9492 and 0.9317), outperforming state-of-the-art clustering methods. A four-layer IoT prototype further demonstrated real-time feasibility with 1–2 second latency, confirming scalability for healthcare deployment.