Mining Sleep Data Towards Efficient Identification and Treatment
Location
Room 232, Schewel Hall
Access Type
Open Access
Presentation Type
Oral presentation
Entry Number
2394
Start Date
4-16-2025 10:30 AM
End Date
4-16-2025 10:45 AM
School
School of professional and Applied Sciences
Department
Computer Science
Keywords
Health, Statistics, Database, Programming, Data
Abstract
The aim of this study is to identify predictors of different sleep disorders which are less studied within the field. With sleep disorders affecting the overall well being of millions of individuals worldwide this is an important examination of potential factors which can lead to the development of such disorders. A relational database was constructed using MySql and Python in order to perform multiple different statistical analyses on data provided by the National Health and Nutrition Examination Survey (NHANES) dataset. Correlations and predictions between described sleep disorders and different physiological factors described within the dataset were found to establish conclusions as to which lesser explored factors are the most related in an individual developing such disorders.
The contribution this work has on the field is identifying potential pitfalls of research being conducted in order to potentially provide better care for patients. Such patients can either be those who already hold a sleep disorder diagnosis or those who hold the factors identified within this study that were proven to have strong relations to the development of a sleep disorder.
Primary Faculty Mentor(s)
Dr. Zakaria Kurdi Dr. Randy Ribler
Primary Faculty Mentor(s) Department
Computer Science
Additional Faculty Mentor(s)
Dr. Rachel Willis
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Mining Sleep Data Towards Efficient Identification and Treatment
Room 232, Schewel Hall
The aim of this study is to identify predictors of different sleep disorders which are less studied within the field. With sleep disorders affecting the overall well being of millions of individuals worldwide this is an important examination of potential factors which can lead to the development of such disorders. A relational database was constructed using MySql and Python in order to perform multiple different statistical analyses on data provided by the National Health and Nutrition Examination Survey (NHANES) dataset. Correlations and predictions between described sleep disorders and different physiological factors described within the dataset were found to establish conclusions as to which lesser explored factors are the most related in an individual developing such disorders.
The contribution this work has on the field is identifying potential pitfalls of research being conducted in order to potentially provide better care for patients. Such patients can either be those who already hold a sleep disorder diagnosis or those who hold the factors identified within this study that were proven to have strong relations to the development of a sleep disorder.