Mental Health Classifier Using Support Vector Machines

An individual’s quality of life may be significantly impacted by mental health conditions like anxiety, depression, and substance misuse. Yet, the stigma associated with mental illness frequently discourages people from seeking help, and the mental health care system struggles to provide effective care due to several technical issues. To address these challenges, this study proposes a mental health analysis system that utilizes a Support Vector Machine (SVM) model to detect whether a user needs a therapist or not. The system was trained and tested on a dataset consisting of mental health records, and the SVM model outperformed other machine learning models with an accuracy rate of 83%. This system can be used as a screening tool for mental health professionals to identify individuals who may require professional help, enabling early intervention and improving the overall mental health of individuals. Addressing the challenges in the mental health care system requires the integration of innovative technological solutions, increasing access and affordability, and reducing the stigma surrounding mental illness.