Identifying Factors Related to Sleep Disorders among Adults in NHIS 2022
Authors
Yiwen Zhou
Share
Annotation
Sleeping is a fundamental aspect of our daily lives. Sleep deprivation, such as dyssomnia, parasomnias, or insomnia, can have far-reaching implications. The lack of sleep can be due to plenty of reasons. Setting an application to predict people’s potential risks of developing sleep disorders has become a pragmatic and intricate endeavor.
This paper discusses factors related to sleep disorders among adults using data from the National Health Interview Survey (NHIS) in 2022. Sleep deprivation, like insomnia, adversely affects mental and physical well-being. This study uses logistic regression along with regularization and cross-validation to analyze the data, considering variables like sex, medication usage, trouble staying asleep, feeling well-rested, and hours of sleep. The model performance is shown with a confusion matrix and a ROC curve analysis. This study also examines the correlation between variables and features' importance, highlighting variables like trouble staying asleep (SLPSTY_A) and feeling well-rested (SLPREST_A) as significant factors related to sleep disorders.
The conclusion is that variables such as sleep difficulties (SLPSTY_A) are significant, while waking up feeling well rested (SLPREST_A) is less critical. Overall, this article provides an overview of the research methods, findings, and factors related to adult sleep disorders.
Keywords
Authors
Yiwen Zhou
Share
References:
[1] “Brain Basics: Understanding Sleep.” National Institute of Neurological Disorders and Stroke, www.ninds.nih.gov/health-information/public-education/brain-basics/brain-basics-understanding-sleep. Accessed 17 July 2023.
[2] Centers for Disease Control and Prevention. (2022, March 20). NHIS - Questionnaires. https://www.cdc.gov/nchs/nhis/quest_doc.htm.
[3] Effect of Sleep Deprivation on the Working Memory-Related N2-P3 Components of the Event-Related Potential Waveform.
https://www.frontiersin.org/articles/10.3389/fnins.2020.00469/full
[4] Brownlee, Jason. “A Gentle Introduction to K-Fold Cross-Validation.” MachineLearningMastery.Com, 2 Aug. 2020, machinelearningmastery.com/k-fold-cross-validation/.