Online Program

Using Simulation and SVM Methods to Determine the Relationship Between U.S. County-Level Adult Obesity Rate and Its Risk Factors

Wednesday, November 4, 2015 : 1:30 p.m. - 1:50 p.m.

Chau-Kuang Chen, Ed.D., School of Graduate Studies, Meharry Medical College, Nashville, TN
The adult obesity rate doubled within the past two decades based on data from the Center for Disease Control (CDC). Simulation and support vector machine (SVM) models were conducted to determine the relationship between U.S. county-level adult obesity rate and its risk factors. Simulation model was run on the initial analysis for multiple linear regression function. The SVM model seeks an optimal hyperplane to separate data from different classes through the computational shortcut of kernel functions. The outcome variable was the adult obesity rate. All risk factors were categorized into four domains of the social ecological model: biological/behavioral factor, socioeconomic status, food environment, and physical environment. Of the 23 risk factors related to adult obesity, the most influential eight factors were identified including physical inactivity, natural amenity, median household income, and percent of all restaurants being fast food. The study results from simulation and SVM models were consistent with those in the literature. This study accomplished its objective by confirming the research hypothesis that the obesity epidemic in all U.S. counties exhibits discernable patterns affected by four domains of social ecological framework: biological or behavioral factor, socioeconomic status, food environment, and physical environment. By analyzing multiple risk factors of obesity in the communities, may lead to the proposal of more comprehensive and integrated policies and intervention programs to solve the population-based problem.

Learning Areas:

Biostatistics, economics
Public health or related research

Learning Objectives:
Demonstrate the usefulness of simulation and SVM models Describe the most influential risk factors that contribute to U.S. adult obesity

Keyword(s): Obesity, Statistics

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am a Biostatistics professor who has years of teaching experiences. My research area includes simulation methods applicable to the study of multiple levels of risk factors in social ecological theory contributing to adverse health outcomes such as infant mortality, syphilis, and obesity, as well as generalized linear models, time series analyses, and artificial intelligence models.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.