Mental Health Prediction Using Artificial Intelligence

Mental Health Disorders have become a significant public health concern worldwide, necessitating accurate and timely diagnostic methods. This study aims to predict the type of Mental Disorder using Artificial Intelligence, specifically the Random Forest Algorithm which is known for its effectiveness in classification tasks. The motivation for this study is the lack of a model which can accurately predict the type of mental health disorder of any person. The main objective of ‘mental health prediction’ is to predict the mental health of a patient on the basis of symptoms only and diagnose the exact disease in order to resolve the serious issues related to mental health which are ignored by society by considering disturbed mental health as a taboo. This paper makes a survey of various mental health symptoms and problems related to it in our society which are solved using AI technologies. To test the performance of our proposed system, we used several machine learning algorithms like Support Vector Machines (SVMs) and Random Forest (RF) algorithms. Here, these algorithms are mainly used for diagnosing mental health disorders on the basis of given input (i.e. verified dataset of symptoms). The Random Forest Model achieved an overall accuracy of 95% in predicting the type of mental disorder. Gain in the values of Precision, Recall, and F1-score was also noted. This model is basically a chatbot which predicts accurately the type of mental disorder of a person, if any. We can expect outcomes such as early detection of any mental disorder, facilitating all self-diagnosis through this bot, and free interaction of the patients with the bot through this model.