| Summary: | Background: Sleep disturbances are a major issue, nowadays, make the whole scientific community to be alert, utilizing machine learning techniques to predict its underlying determinants. Objective: The main purpose of this paper is to test the accuracy of machine learning algorithms in interpretation of sleep problems. Methods: A public dataset was used and multiple feature selection techniques were addressed to identify the most influential predictors in sleep disturbances. Explainable AI was used to further interpret how each predictor impacts individual predictions. Results: Results from model performance show that AdaBoost outperformed other models (71.27% accuracy) and sleep quality is the dominant predictor (with SHAP value 0.01586), indicating the strongest influence on model. Conclusion: The incorporation of explainable AI methods (e.g., SHAP) enhances the clinical and public health value of these models, enabling healthcare providers to target specific interventions and potentially improve patients' sleep health outcomes.
|
|---|