A Novice Approach to Predict Obsessive Compulsive Disorder by using AIML Techniques

Obsessive Compulsive Disorder is a serious psychological health condition featured by continuous thoughts and monotonous episodes, accordingly, impacting one’s daily life condition. With Early plus accurate prediction of OCD can lead to immediate diagnosis and better patient outputs. This analysis focusses to attain a model for OCD using machine learning techniques applied to a comprehensive dataset comprising demographic, clinical, and behavioural data. We gathered datasets from a varied group of participants, which included individuals with OCD as well as a control group without the disorder. Several machine learning algorithms were implemented and evaluated based on their predictive accuracy. The results highlight the potential of machine learning to improve early OCD diagnosis, providing clinicians with a promising tool to help create personalized treatment plans.