How Data Science is Used in Early Mental Health Risk Detection

Nisma M R , Nhyvija J , Safna S , Karthika M

Data Science, Mental Health Analytics, Early Risk Detection, Machine Learning, Natural Language Processing, Predictive Analytics, Ethical AI.

Early detection of mental health risks plays a vital role in preventing severe psychological disorders and improving overall well-being. The increasing availability of digital data from social media platforms, wearable devices, and electronic health records has enabled the application of data science techniques for early mental health risk identification. This paper examines how data science methods, including machine learning and natural language processing, are utilized to analyze behavioral, emotional, and physiological data for detecting early indicators of mental health conditions such as depression and anxiety. Key features such as linguistic patterns, activity levels, sleep behavior, and stress indicators are extracted to build predictive models capable of identifying potential risks before clinical diagnosis. The study also highlights the challenges associated with data privacy, ethical concerns, and model interpretability. Compared to traditional mental health assessment methods, data-driven approaches provide continuous monitoring and timely alerts, supporting mental health professionals in early intervention and personalized care. This work emphasizes the potential of data science to enhance proactive mental healthcare systems and promote improved mental health outcomes.

Published Paper   E-Certificate

"How Data Science is Used in Early Mental Health Risk Detection", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.11, Issue 1, page no.a904-a907, January-2026, Available :https://ijsdr.org/papers/IJSDR2601096.pdf

Volume 11 Issue 1, January-2026

Pages : a904-a907

Paper Reg. ID: IJSDR_306939

Published Paper Id: IJSDR2601096

Downloads: 000253

Research Area: Science and Technology

Country: Kanniyakumari, Tamil Nadu, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2601096

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2601096

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition