Modeling Sleep Health and Lifestyle Using Supervised Learning Algorithms

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ID: 312743
2025
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Abstract
With the realization of the importance of sleep quality as an indicator of general well-being, this work uses the strength of machine learning to discover significant trends in information about lifestyles and health to make predictions regarding sleep health. Based on the Sleep Health and Lifestyle Dataset that consists of 373 instances (rows) and 13 features (columns), including demographic, physiological, and lifestyle-related data, one can classify the target variable Quality of Sleep as a categorical attribute, defining the task in terms of classification. Different machine learning models were implemented and compared by means of precision, recall, accuracy, and F1 score: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest, Logistic Regression, Decision Tree, Gradient Boosting, Naive Bayes, and AdaBoost. Of these, the models of the ensemble type were better performers, and the Random Forest model produced the best outcomes in all measures: 98.67% accuracy, 98.74% precision, 98.67% recall, and 98.66% F1 score. The other schemes, Decision Tree and Gradient Boosting, also performed well, and SVM received the lowest scores. The results emphasize the usefulness of ensemble methods in modeling complicated and non-linear relationships in multifactor health data. The results reinforce the possibilities of machine learning in relation to data-driven, individualistic health-related recommendations and early interventions during sleep health management.
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imported_1777056425_69ebbaa958612 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors M Usman Bhatti, Ali Saeed, Muhammad Ashir, Naveed Hussain, Mehmood Anwar, Muhammad Farhat Ullah
Journal Journal of Computing & Biomedical Informatics
Year 2025
DOI
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URL
https://jcbi.org/index.php/Main/article/view/998
Keywords Keywords not found

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