Mental Health Platform

Abstract

Modern lifestyle challenges are leading to the very fast rise of mental health issues like stress, anxiety and depression. Detecting these problems in early stage and monitoring are crucial to prevent worsening situations. Here, we introduce “Mental Health Platfrom”, a digital mental health platform that processes user behavior/activities using data-driven techniques for early provision of support. The system accumulates latent patterns (such as emotion, activity, and self-assessment metrics) of user behavior that does not identify the individual. Emotional trends and potential prediction are detected using machine learning algorithms mental health risks. It is well known for its personalized recommendations, self help resources and professional guidance options. The findings showed users appear to have gained greater awareness and earlier identification of a mental health condition. DATA 2 DRESS is a system that incorporates the technological gap between mental health and supply.

Introduction

This paper presents a Mental Health Platform that uses machine learning to support the early detection of mental health conditions such as stress, anxiety, and depression. The platform aims to improve access to mental health support by providing an accessible, anonymous, and continuously available digital solution that analyzes user behavior and offers personalized recommendations.

The system collects anonymized user data, including mood tracking entries, sleep patterns, activity levels, and questionnaire responses. Before analysis, the data undergoes preprocessing through missing value handling, normalization, and feature extraction to improve model performance.

Three supervised machine learning algorithms are evaluated:

• Support Vector Machine (SVM) for stress prediction,
• Decision Tree for anxiety prediction, and
• Random Forest for depression prediction.

The platform follows a simple workflow in which users submit their information, the data is processed, the trained model predicts potential mental health conditions, and the system provides appropriate suggestions and mental health resources. Its architecture consists of a frontend for user interaction, a backend for model execution, and a database for storing user information.

The literature review highlights the growing importance of digital mental health platforms in addressing challenges such as stigma, limited accessibility, and high treatment costs. It also discusses the role of artificial intelligence in enabling personalized support and early detection while emphasizing concerns related to data privacy, ethics, and clinical validation.

Experimental results demonstrate promising performance, with Random Forest achieving the highest accuracy (91%) for depression prediction, followed by SVM (85%) for stress detection and Decision Tree (82%) for anxiety prediction. The study concludes that the platform can increase users' awareness of their mental health, identify early warning signs, and provide actionable insights, with Random Forest proving to be the most effective algorithm due to its superior predictive accuracy.

Conclusion

The “Mental Health Platform” is a successful example of utilizing digital solutions for mental health tracking and early intervention. Combining machine learning techniques with user-friendly interfaces, the system helps tailor support to ensure personal mental health monitoring and awareness. This unique data-driven study brings to light the challenges in developing mental disorders because of their years-long identification process, and many people may not even realize they have one solution.

References

[1] T. Mohana Priya et al., “Machine Learning Algorithm for Stress Prediction,” Global Journal of Computer Science, 2020. [2] Ganesh Kumar, “Artificial Neural Networks for Detection,” IJCSNS, 2015. [3] Gyusoo Kim, “Payment Research,” Bank of Korea, 2015. [4] Chengwei Liu et al., “Fraud Detection using Random Forest,” IJEF, 2015. [5] World Health Organization, “Mental Health Report,”

Copyright

Copyright © 2026 Pallavi Sonawane, Shurta Shinde, Aatik Shaikh, Pankaj Sanap, Prof. Anita Yadav. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.