DETECTING MENTAL HEALTH DISORDERS USING MACHINE LEARNING

Abstract

The need for reliable and effective early detection technology has been fueled by the rising prevalence of mental health issues. This study provides a machine learning-based approach to detect mental health problems using methods such as Support Vector Machines (SVM), Random Forest, and Logistic Regression. The system is made up of two parts: the administrative module and the user module. The Admin module is responsible for uploading, preprocessing, partitioning, and executing machine learning models for analysis. Another part of it is making comparison graphs to demonstrate the efficacy of various methods. Customers may register, log in, and predict their mental health status using the User module, which is based on the taught models. The initiative aims to provide a user-friendly platform for mental health detection by utilizing machine learning to improve early diagnosis.