<scp>MH</scp> ‐ <scp>XAI</scp> : Hybrid Deep Learning and <scp>XGBoost</scp> Explainable <scp>AI</scp> Model for Mental Health Prediction

ABSTRACT Integrating deep learning with interpretable machine learning methods offers significant benefits for predicting mental health risks. This research introduces a hybrid architecture MH‐XAI, aimed at precisely predicting mental health while providing transparent, feature‐level explanations. MH‐XAI integrates a multi‐scale 1D Convolutional Neural Network (CNN), channel‐wise attention mechanisms, an ensemble of (XGBoost) classifiers, and SHAP (SHapley Additive exPlanations) to achieve both local and global interpretability. The model is trained on a comprehensive dataset derived from Open Sourcing Mental Illness (OSMI) Mental Health in Tech surveys conducted between 2016 and 2023. This dataset encompasses a variety of demographic, psychological, and occupational characteristics. The input features are restructured into a format that facilitates deep convolutional learning. The CNN feature extractor utilizes parallel convolutional layers with three different kernel sizes, allowing the model to capture both short‐range and long‐range dependencies in tabular data. The multi‐scale representations are subsequently enhanced using a channel‐wise attention process. The acquired features are transmitted to an ensemble of XGBoost classifiers for enhanced prediction accuracy. MH‐XAI attains a test accuracy of 91.54%, F1‐score of 92%, precision of 92%, and recall of 91%, surpassing standalone CNN and XGBoost. SHAP elucidates the model's predictions by quantifying the contributions of each feature. Results underscore critical factors such as past mental health history, workplace culture, current mental health, and family history that affect outcomes. MH‐XAI provides a precise, scalable, and comprehensible solution for the early detection of mental health in workplace environments.