A Multi-Method Machine Learning Analysis of Sleep Disturbances' Determinants During COVID-19...23rd International Conference on Informatics, Management and Technology in Healthcare (ICIMTH 2025), July 4-6, 2025, Athens, Greece.
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 interpretatio...
| Publicado en: | Studies in Health Technology & Informatics Vol. 328; pp. 106 - 111 |
|---|---|
| Autores principales: | , , , |
| Formato: | proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2025
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=186368368&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 186368368 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2025 vid: 328 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 186368368 186368368 186368368 10.3233/SHTI250682 186368368 ppf: 106 ppct: 5 formats: tig: atl: A Multi-Method Machine Learning Analysis of Sleep Disturbances' Determinants During COVID-19...23rd International Conference on Informatics, Management and Technology in Healthcare (ICIMTH 2025), July 4-6, 2025, Athens, Greece. aug: au: LIALIOU, Paschalina VOUZIS, Eleftherios MAGLOGIANNIS, Ilias MANTAS, John affil: Department of Digital Systems, University of Piraeus, Greece sug: subj: Machine Learning Methods COVID-19 Pandemic Psychosocial Factors Sleep Disorders Psychosocial Factors Social Determinants of Health Evaluation Sensitivity and Specificity Algorithms Congresses and Conferences Greece Greece Human Male Female Models, Statistical Multimethod Studies Data Analysis Sleep Quality Evaluation Health Status Jordan Anxiety Psychosocial Factors Depression Psychosocial Factors Physical Activity Scales Questionnaires Sociodemographic Factors Appetite Psychosocial Factors Sleep Duration Univariate Statistics Chi Square Test Random Forest Logistic Regression Age Factors Sex Factors Descriptive Statistics Data Analysis Software Male Female ab: 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. pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|