Machine Learning Models based Mental Health Detection

Abstract

This century will be the fastest ever, putting a heavy burden on future generations, especially students. Future generations will face enormous stress, competition, social issues, and constant pressure. Their lives will become a race. That leaves students with mental health issues that lead to disorders. Five of the most common types of disorders that young people especially face are bipolar disorder (mood disorders), anxiety disorders, depression, eating disorders, and sleep issues. As machine learning plays a vital role in the easiness of human life, this paper also uses Machine Learning (ML) algorithms to screen Mental Health by using a Mental Disorder Questionnaire (MDQ). In this research, there are two types of Questionnaires employed. The first Self Reporting Questionnaire-15 (SRQ-15) has 15 general mental disorder questions with the option of Yes/No. The second Self Reporting Questionnaire-25 (SRQ-25) has 25 questions, five questions for each of the five different mental health disorders mentioned. Within each section, the user fills the questionnaire according to the instructions. We labeled the train data set using Supervised Machine Learning. So we use different algorithms to compare results with manual testing.