Date of Award
Summer 8-11-2020
Degree Type
Thesis
Degree Name
Master of Public Health (MPH)
Department
Public Health
First Advisor
David Ashley
Second Advisor
Dora Ilyasova
Third Advisor
Ruiyan Luo
Abstract
This study investigates three alternative machine learning methods to explore influential predictors of type 2 diabetes. It compares ridge, lasso, and elastic net regression to linear regression, and focuses on 12 outcome variables that include age, sex, race, income, education level, body mass index, waist circumference, arm circumference, hip circumference, family history, smoking status, sleep duration, high blood pressure, and high-density lipoprotein. Ridge, lasso and elastic net regression do not outperform linear regression but do assist in choosing a simpler model which could be important for improving future modeling.
DOI
https://doi.org/10.57709/18768476
Recommended Citation
Chernishkin, Amanda E., "Identifying Influential Variables in the Prediction of Type 2 Diabetes Using Machine Learning Methods." Thesis, Georgia State University, 2020.
doi: https://doi.org/10.57709/18768476
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