As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
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.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.