CounselEase: Predictive Mental Health Support

Abstract

Problem: There are various reasons that often lead to the neglect of mental health issues. These include the aspects of social stigma, lack of time, and limited scope of traditional face-to-face counseling. Most of the online counseling sites available do not utilize machine learning to determine the probable risk of mental health issues arising from certain user’s symptoms nor do they give recommendations of suitable counselors as well as put in place strict confidentiality. This project seeks to overcome such deficiencies through the creation of an online counseling platform which is able to anticipate mental health disorders from the users’ symptoms and suggest suitable counselors. Methodology: An Agile development approach quickly validated the design through the use of both quantitative and qualitative methods. A machine learning structure is embedded in the platform, which will train on a symptom-disease database, and this enhances the predictive ability of the platform. User surveys and interviews were conducted to elicit requirements so that the platform will effectively address user needs regarding ease of access and confidentiality. Areas of concern during development involved feature development and training and testing of predictive models to enhance precision in the predictions and user interest. Initial Results: The results obtained by the prototype were encouraging, with a prediction accuracy of mental health conditions at 89%, an AUC-ROC of 0.91 and a 5% false positive rate. These outcomes corroborate the platform’s reliability in offering predictive matrices for mental health disability and recommending counselors’ services to users at first informs them about the mental health problem aiding in identification. Further testing is ongoing to enhance model precision and lower the occurrence of false positive rates.