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@article{196368,
        author = {Mr. P. Chaitanya and P. Dimpul and R. Jyothi Naga Vimala and V. Amrutha and S.M.S.N.B. Ratna Sri and A. Tarun},
        title = {Explainable Mental Health Risk Stratification and Intervention Recommendation Framework for Academic and Early-Career Populations},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {3250-3254},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196368},
        abstract = {Mental health issues among students and early-career individuals have increased due to academic pressure, work stress, and changes in lifestyle. Identifying people who are at risk at an early stage is important so that proper support can be provided.
In our project, we developed a system to analyse mental health survey data and understand behavioural patterns. We used K-Means clustering to group individuals based on similar characteristics such as stress levels, sleep patterns, and daily habits. This helps in identifying different behavioural groups present in the dataset.
After grouping the data, we applied the Random Forest algorithm to predict mental health risk levels such as low, moderate, and high. The model also helps in identifying which factors, like stress and sleep, have the most impact on mental health.
Based on the predicted results, the system can suggest suitable actions such as self-care, stress management, or professional consultation. The results show that combining clustering and classification improves understanding of mental health patterns and helps in better risk prediction. This approach provides a simple and effective way to analyse mental health data and support early identification},
        keywords = {Mental Health Analysis, Machine Learning, K-Means Clustering, Random Forest, Behavioural Patterns, Risk Prediction},
        month = {April},
        }