Presentation Type
Poster
Start Date
5-4-2022 11:00 AM
End Date
5-4-2022 1:00 PM
Subjects
sleep, work, machine learning
Advisor
Tori Crain
Student Level
Doctoral
Abstract
The present study will utilize machine learning methods to find the individual factors (e.g, demographic, lifestyle, contextual, work-related) best predict sleep. I will examine (1) whether machine learning techniques better predict sleep over linear prediction methods and (2) what factors are most important when predicting sleep. Using government-funded data from the Bureau of Labor Statistics (BLS), a variety of prediction methods will be utilized to evaluate and compare predictive performance across 2018, 2019, and 2020. Results found will have a variety of theoretical and practical implications for scientists and practitioners.
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Persistent Identifier
https://archives.pdx.edu/ds/psu/37475
Included in
Understanding Work and Sleep Through A Machine Learning Approach
The present study will utilize machine learning methods to find the individual factors (e.g, demographic, lifestyle, contextual, work-related) best predict sleep. I will examine (1) whether machine learning techniques better predict sleep over linear prediction methods and (2) what factors are most important when predicting sleep. Using government-funded data from the Bureau of Labor Statistics (BLS), a variety of prediction methods will be utilized to evaluate and compare predictive performance across 2018, 2019, and 2020. Results found will have a variety of theoretical and practical implications for scientists and practitioners.