MINDCURE: Website to guide your Mental Health using Machine Learning
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Abstract
The research introduces "MINDCURE," a cutting-edge web platform designed for young adults, leveraging ReactJS and advanced machine learning to assess mental health using the DASS21 questionnaire. By gauging levels of depression, anxiety, and stress, MINDCURE provides tailored activities to support mental well-being. At its core, the platform employs decision tree models for precision and a Django REST API framework for seamless frontend-backend interaction, all anchored to a MySQL database for robust data management. While primarily serving individual users, its architecture holds promise for broader research and education applications. Standing distinct in the digital mental health arena, MINDCURE combines technology with a user-centric design, and envisions future integrations like real-time tracking, gamification, and professional collaborations, marking it as a pioneering solution in digital mental health care.
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References
Anderson, M., & Jiang, J. (2018). Teens, social media & technology 2018. Pew Research Center. https://www.pewresearch.org/internet/2018/05/31/teens-social-media-technology-2018/
Lovibond, S. H., & Lovibond, P. F. (1995). Manual for the Depression Anxiety Stress Scales (2nd ed.). Psychology Foundation.
Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS quarterly, 28(1), 75-105.
Breiman, L., Friedman, J., Stone, C. J., & Olshen, R. A. (1986). Classification and regression trees. CRC press.
Facebook Inc. (2019). Create a New React App. React documentation. https://reactjs.org/docs/create-a-new-react-app.html
Oreilly, S. (2015). Django REST framework. Django. https://www.django-rest-framework.org/
W3C. (2018). Cascading Style Sheets. World Wide Web Consortium. https://www.w3.org/Style/CSS/Overview.en.html
Twitters Inc. (2019). Introduction. Bootstrap documentation. https://getbootstrap.com/docs/4.3/getting-started/introduction/
Chollet, F. (2018). Deep learning with Python. Manning Publications Co.
Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011, September). From game design elements to gamefulness: defining" gamification". In Proceedings of the 15th international academic MindTrek conference: Envisioning future media environments (pp. 9-15).
Python Software Foundation. (2019). Welcome to Python.org. https://www.python.org/
McKinney, W. (2012). Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. "O'Reilly Media, Inc.".
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
Dabbish, L., Stuart, C., Tsay, J., & Herbsleb, J. (2012, February). Social coding in GitHub: transparency and collaboration in an open software repository. In Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work (pp. 1277-1286).
Henning, K., & Bussert, K. (2019). Anxiety, depression and stress in young adults: Optimised management using a primary care online mental health tool. Primary Health Care Research & Development, 20.