Machine learning-based fatigue classification using heart rate variability and cortisol: A multimodal approach to wearable health monitoring.

Objective: Fatigue is a critical indicator in modern health management, and efficient, accurate methods for predicting fatigue levels using wearable devices have garnered increasing attention. Although recent advancements have enabled non-invasive cortisol measurement via wearable sensors, it remains unclear how effectively cortisol, in combination with other physiological biomarkers, predicts fatigue. Therefore, this study aimed to evaluate the effectiveness of a multimodal machine learning model that integrates cortisol levels and heart rate variability (HRV) for fatigue prediction. Methods: Data from 336 participants who completed the Fatigue Severity Scale (FSS) were analyzed. Missing data mechanisms for cortisol were examined, and multivariate imputation by chained equations (MICEs) were applied. A TabNet deep-learning model was used to predict low and high fatigue levels based on HRV and cortisol data. Results: The model using only HRV variables achieved a test AUC of 0.774, whereas the model incorporating both HRV and cortisol levels achieved 0.741, indicating a minimal overall performance difference. Feature importance analysis revealed that, in the cortisol-included model, predictions relied on a limited set of features. When feature selection was applied to this model, a reduced set of variables-age, cortisol, and logarithmic very low frequency-achieved comparable predictive performance (AUC = 0.759) without performance degradation. Conclusion: This study demonstrated that a fatigue prediction model based on cortisol and HRV can maintain significant predictive power with a reduced number of variables. These findings suggest the potential for practical implementation in wearable devices, enabling accurate fatigue monitoring while minimizing sensor count and computational burden.