AI Driven Self Diagnosis of Mental Health Status Using Machine Learning Models
Pallavi M O
School of Computer Science and Engineering, Reva University, Bangalore, Karnataka, India, Department of Artificial Intelligence and Data Science, Sapthagiri NPS University, Bengaluru, Karnataka, India.
The increasing prevalence of mental health issues worldwide has underscored the need for innovative, accessible diagnostic tools. Traditional mental health assessment methods often rely on self-reporting and professional evaluations, which is time-consuming and lack immediacy. Paper presents an AI-driven framework for the self-diagnosis of mental health status, leveraging machine learning models trained on a variety of user-generated data, including social media activity, profile characteristics, and social connections. By integrating feature engineering techniques, dimensionality reduction, and robust data preprocessing, the proposed model can detect and predict mental health states with considerable accuracy. The novelty of this work lies in its automated, real-time processing pipeline, which combines initial data validation and label assignment with advanced ML techniques for effective prediction. This framework offers users insights into their mental well-being and also serves as a scalable solution for mental health monitoring, potentially reducing the burden on healthcare systems by facilitating early intervention.
Keywords
AI-Driven Diagnostics, Mental Health Prediction, Machine Learning Models, Self-Diagnosis Tools, Social Media Analysis, Real-Time Processing.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Pallavi M O and Pundalik Chavan;
Methodology: Pallavi M O;
Software: Pundalik Chavan;
Data Curation: Pallavi M O;
Writing- Original Draft Preparation: Pallavi M O and Pundalik Chavan;
Visualization: Pallavi M O;
Investigation: Pundalik Chavan;
Supervision: Pallavi M O;
Validation: Pundalik Chavan;
Writing- Reviewing and Editing: Pallavi M O and Pundalik Chavan;
All authors reviewed the results and approved the final version of the manuscript.
Acknowledgements
Author(s) thanks to Dr. Pundalik Chavan for this research completion and support.
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No funding was received to assist with the preparation of this manuscript.
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Corresponding author
Pallavi M O
School of Computer Science and Engineering, Reva University, Bangalore, Karnataka, India, Department of Artificial Intelligence and Data Science, Sapthagiri NPS University, Bengaluru, Karnataka, India.
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Cite this article
Pallavi M O and Pundalik Chavan, “AI Driven Self Diagnosis of Mental Health Status Using Machine Learning Models”, Journal of Machine and Computing, vol.6, no.1, pp. 181-194, 2026, doi: 10.53759/7669/jmc202606013.
