MindEase : AI based Stress Management System

Registration ID: IJNRD_322371

Published ID: IJNRD2603440

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Shalini Singh, Anshika Reja, Aditya Kumar Pandey, Rohit Kumar Singh, Sourabh Tiwari

Machine Learning, Stress Prediction, Random Forest, Mental Health, Data Science, Flask, Web Application, Predictive Analytics, Healthcare Technology

Mental stress has become a major global concern due to rapid urbanization, fast-paced lifestyles, and increasing societal pressures. As reported by global health bodies, stress contributes to anxiety, depression, cardiovascular issues, and reduced productivity. Traditional stress assessment methods depend heavily on self-reporting or occasional medical visits, which often fail to provide continuous or personalized insights. To address these challenges, Mental Stress Manager presents an AI-powered web application designed to predict user stress levels by analyzing a combination of physiological, behavioral, and lifestyle-related factors. Users provide data such as age, gender, occupation, BMI category, sleep duration, sleep quality, physical activity levels, heart rate, daily steps, and blood pressure. These inputs are processed using a RandomForestRegressor model trained on a curated dataset, enabling accurate classification of stress into Low, Medium, or High levels. The system also delivers personalized recommendations aimed at improving sleep, activity, and overall mental well-being. The platform integrates secure user authentication, a responsive interface built with HTML5, CSS3, and Bootstrap 5, and a Flask backend responsible for server-side logic, API handling, and model inference. Data preprocessing techniques—including cleaning, outlier detection, encoding, and correlation analysis—ensure high-quality model performance, evaluated using metrics like MAE and R² Score. Deployment on cloud platforms like Heroku or Vercel provides accessibility, while SQLite maintains user prediction histories. Pilot testing with over 800 users demonstrated strong predictive accuracy, a user-friendly interface, and high engagement, reflecting the system’s potential to raise awareness and encourage healthier habits. Aligned with initiatives like India’s Mental Health Policy and the UN’s SDG 3, the project highlights how data science and web technologies can support preventive mental healthcare. Future enhancements, including mobile app development, smartwatch-based real-time data integration, and NLP-driven mood tracking, position Mental Stress Manager as a scalable, impactful solution for promoting mental wellness.

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Shalini Singh, Anshika Reja, Aditya Kumar Pandey, Rohit Kumar Singh, & Sourabh Tiwari (March-2026). MindEase : AI based Stress Management System. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(3), e269-e274. https://ijnrd.org/papers/IJNRD2603440.pdf

Volume 11 Issue 3, March-2026

Pages : e269-e274

Paper Reg. ID: IJNRD_322371

Published Paper Id: IJNRD2603440

Research Area: Health Science All

Author Type: Indian Author

Country: bhopal, Madhya Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2603440.pdf

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