Mental Health Tracker – AI Enabled Depression Level Detector

DOI

10.2991/978-94-6463-858-5_9How to use a DOI?

Keywords

Depression Prediction; Machine Learning; Mental Health Support; Support Vector Machines; Personalized Solutions; Early Intervention

Abstract

Mental health disorders, especially those of depression, are now a concern at the universal level which necessitates an approach for early diagnosis and treatment options that are accessible and technology-based. The paper presents a web-based depression screening and support system that aims to provide its users with customized mental health information through machine learning and artificial intelligence (AI). The system uses several classification algorithms, including Support Vector Machines (SVM), Random Forests, Decision Trees, Gaussian Naïve Bayes, and K-Nearest Neighbors (KNN), to predict levels of depression from user answers to a standardized questionnaire. By analyzing patterns in user responses, the system incrementally increases the accuracy of the assessment of depression severity.

Once the depression level has been established, the platform customizes its suggestions through Gemini AI to include lifestyle changes, psychotherapy exercises, physical activities, and nutrition tips that benefit mental wellness. The system also contains yoga videos and posts on mental health for wholesome support. A separate section caters to those in need of more professional help through contact numbers of health workers dealing with mental healthcare. The program aims to connect technology with mental health care on an interactive level using AI for the assessment and management of depression. It is considered to be an easy-to-understand and accessible platform for those in need of mental health support so that early intervention leads to a better well-being.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Amruta Amune
AU  - Vivek Raut
AU  - Om Sangole
AU  - Aayush Tolmare
AU  - Vineet Wathurkar
AU  - Rohit Yeole
PY  - 2025
DA  - 2025/11/04
TI  - Mental Health Tracker – AI Enabled Depression Level Detector
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 88
EP  - 100
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_9
DO  - 10.2991/978-94-6463-858-5_9
ID  - Amune2025
ER  -