Early Detection of Mental Health and Burnout Indicators Using Machine Learning, Transformers, and Explainable AI (XAI

Mental health disorders and occupational burnout are major public health concerns that frequently go un- detected until they cause severe psychological or physical impairment. Conventional screening relies on self-reported questionnaires and clinical interviews, which are reactive, prone to reporting bias and stigma, and difficult to scale. This paper proposes a hybrid framework for the early detection of mental health and burnout indicators from textual, be- havioral, and physiological signals. Transformer-based language models extract semantic and affective cues from text (e.g., journal entries, social media posts, workplace communication); these representations are fused with structured behavioral features such as sleep patterns, activity levels, and communication frequency before classification by classi- cal and ensemble machine learning models. To address the black-box nature of deep models and foster clinical trust, SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) provide global and instance-level interpretations of each prediction. The pipeline is evaluated on public benchmark text data and simu- lated behavioral data; [insert headline result, e.g., best model with F1 and AUC-ROC]. The framework is intended as a transparent, human-in-the-loop screening aid for clinical and organizational settings.