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

Abstract: 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. Keywords: mental health detection, burnout prediction, machine learning, transformers, natural language processing, explainable AI, SHAP, LIME, multimodal fusion