• Version
• Download 14
• File Size 354.29 KB
• File Count 1
• Create Date 16 April 2025
• Last Updated 16 April 2025

MindTrack: Machine Learning for Mental Health Insights

Authors:

V. Pavan Kumar

Department of CSE (AI&ML) 2111cs020336@mallareddyuniversity.ac.in

S. Pavan Reddy

Department of CSE (AI&ML) 2111cs020338@mallareddyuniversity.ac.in

K. Pavitra Rupa

Department of CSE (AI&ML) 2111cs020340@mallareddyuniversity.ac.in

S. Pavan Reddy

Department of CSE (AI&ML) 2111cs020337@mallareddyuniversity.ac.in

G. Pavan Sai

Department of CSE(AI&ML)

2111cs020339@mallareddyuniversity.ac.in

Prof. P. Bhavani

Department of CSE (AI&ML) School of Engineering

MALLA REDDY UNIVERSITY

HYDERABAD

Abstract: - Mental health disorders affect millions of individuals worldwide, emphasizing the urgent need for early detection and intervention. MindTrack: Machine Learning for Mental Health Insights is an innovative tool designed to predict and assess mental health conditions such as depression, anxiety, and stress using advanced machine learning techniques. The system leverages user inputs, including textbased responses, survey data, and optional behavioral metrics, to analyze patterns and identify potential mental health risks. Preprocessed data is evaluated through robust algorithms, including natural language processing (NLP) models for text analysis and statistical models for numeric inputs, ensuring high accuracy and sensitivity. MindTrack offers actionable insights, such as self-care tips, progress tracking, and professional recommendations, all while ensuring data privacy and ethical standards. By integrating artificial intelligence with mental health care, the project aims to empower individuals and healthcare providers to address mental well-being proactively. MindTrack aims to bridge the gap between technology and mental health care, fostering a more inclusive and proactive approach to mental well-being.

Keywords: Mental Health Detection, Machine Learning, Psychological Assessment, Behavioral Insights, Supervised Learning, Feature Engineering, Model Optimization, Hyperparameter Tuning, Front-End Integration, GUI-Based Input, Predictive Analytics, Health Monitoring, Data-Driven Insights, Depression.