Predicting Mental Health Stress Levels Through Sleep Patterns using Ensemble Models and Explainable AI

This study investigates the potential of Explainable Artificial Intelligence (XAI) to enhance mental healthcare by improving diagnostic transparency and treatment precision. Using the “Human Stress Detection” dataset from Kaggle, we applied an ensemble of classifiers, including Random Forest, K-Nearest Neighbors, SVM, XGBoost, and Decision Trees, to assess stress levels based on physiological data. The Random Forest model achieved the highest accuracy of 98.41%. By leveraging XAI techniques like LIME and SHAP, the study demonstrates how XAI can improve the interpretability of mental health assessments, reduce biases, and promote more equitable and effective mental healthcare.