Mental Health Detection Using Machine Learning

Authors: Kajal Nikam, Rohini Dawange, Vaibhav Walke, Karan Dabhade

Country: India

Full-text Research PDF File:   View   |   Download

Abstract: Mental health is a crucial aspect of overall well-being, and its early detection and intervention play a pivotal role in preventing severe mental health issues. This system presents an innovative approach to mental health detection using machine learning (ML) techniques. The system categorizes users into three levels: low, medium, and high, based on their mental health status, allowing for personalized recommendations to improve their mental state. To assess the users' mental health levels, the system employs a carefully designed set of multiple-choice questions, which effectively gauge various aspects of their emotional and psychological well-being. These questions consider factors such as mood, stress levels, sleep patterns, and social interactions to provide a comprehensive evaluation. Upon categorization, users receive tailored suggestions and resources to address their specific mental health needs, thereby promoting a proactive and personalized approach to mental health care. This ML-based mental health detection system has the potential to make a significant impact by identifying mental health issues early and guiding individuals towards the necessary support and resources to enhance their well-being.

Keywords: Mental, Machine learning, Suggestions

Paper Id: 230603

Published On: 2024-04-25

Published In: Volume 12, Issue 2, March-April 2024

Cite This: Mental Health Detection Using Machine Learning - Kajal Nikam, Rohini Dawange, Vaibhav Walke, Karan Dabhade - IJIRMPS Volume 12, Issue 2, March-April 2024.

Share this