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@article{199784,
        author = {Ashutosh Sharma and Aditi and Abhishek Bhati and Garv Rawat},
        title = {Well Mind AI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {15781-15786},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199784},
        abstract = {Access to timely psychological support remains limited for many individuals, increasing the need for accessible digital mental health solutions. This paper presents an integrated web-based system designed to help users understand and manage their mental well-being through a combination of clinical screening tools, Machine Learning-based prediction models, and an interactive conversational agent. The platform utilizes standardized instruments such as PHQ-9 and GAD-7, along with behavioural and linguistic features extracted from user input, to estimate emotional well-being. Several models, including SVM, Random Forest, XGBoost, LSTM, and a fine-tuned BERT model, were evaluated and compared. A stacking ensemble approach was then developed to integrate their strengths, achieving an accuracy of 94% and an AUC-ROC score of 0.96, outperforming individual models. In addition to predictive analysis, the system incorporates a chatbot capable of detecting crisis-related language and guiding users toward appropriate support. By combining predictive modelling with real-time conversational assistance, the proposed system demonstrates potential for enhancing early mental health detection and improving accessibility to support for individuals who may not seek traditional clinical services.},
        keywords = {Artificial Intelligence, Mental Health, Machine Learning, Depression Detection, Natural Language Processing},
        month = {April},
        }