Data Driven Mental Health Predicting Depression Using Machine Learning
The rise of information investigation methods such as machine learning is opening up unused openings in the field of mental wellbeing. This department of inquire about, known as data-driven mental wellbeing, is especially interested in applying machine learning to identify discouragement. Analysts trust to make models that can recognize individuals at chance of discouragement by analyzing information such as overview answers, social media action, and discourse designs. Early distinguishing proof is basic for his, her, their, etc. ideal treatment, and machine learning may play an imperative part in fulfilling this point. Be that as it may, it is basic to realize that these models are not aiming to supplant mental wellbeing pros. Instep, they are planning as disobedient to help in screening and intercession efforts.
Authors
- Krishna Murari (ORCID: https://orcid.org/0000-0003-4644-8225)
- Anand Prakash (ORCID: https://orcid.org/0000-0001-8049-8184)
- Neha Kumari (ORCID: https://orcid.org/0009-0006-4268-326X)
- Richa Verma (ORCID: https://orcid.org/0009-0005-5314-8548)
- Anamika Kumari (ORCID: https://orcid.org/0000-0001-8431-0951)
- Nitesh Kumar
Institutions
- Galgotias University (IN)
- National Institute of Technology Jamshedpur (IN)
- Babasaheb Bhimrao Ambedkar Bihar University (IN)
Publication Details
- Published
- 2025-04-17
- DOI
- https://doi.org/10.1109/icdsbs63635.2025.11031961
- Citations
- 3
- Primary Topic
- Mental Health via Writing
- Type
- article
- Field-Weighted Citation Impact
- 7.32