Hybrid Intelligent EEG-based Framework Integrating Deep Learning and Ensemble Methods for Accurate Early OCD Diagnosis

Obsessive-compulsive disorder (OCD) is a persistent mental illness that is associated with intrusive thoughts and repetitive actions that considerably affect the normal functioning of the body. One of the greatest issues in the field is early diagnosis as the symptoms are similar and the conventional methods in the diagnosis involve the use of neuroimaging and EEG data which has limitations. The present research offers a composite machine learning and deep learning model of early OCD detection, referring to applying EEG signal processing methods to the combination of the advanced feature extraction and classification algorithms. The model uses the convolutional neural networks coupled with ensemble learning to enhance diagnostic accuracy and solidity. Large-scale EEG experimental assessment shows that performance is better with accuracy of 99.87% with steady cross-validation performance. It is suggested that the proposed approach will maximize the capability of early diagnosis, decrease the misclassification rates, and offer clinicians with a efficient decision-support system with high reliability. The study will help to enhance mental health assessment and promote the significance of diagnostic solutions based on AI.