Determining the Relationship between U.S. County-Level Adult Obesity Rate and Multiple Risk Factors by PLS Regression and SVM Modeling Approaches
Chau-Kuang Chen
Data from the Center for Disease Control (CDC) has shown that
the obesity rate doubled among adults within the past two
decades. This upsurge was the result of changes in human
behavior and environment.
Partial least squares (PLS) regression and support vector
machine (SVM) models were conducted to determine the
relationship between U.S. county-level adult obesity rate and
multiple risk factors. The outcome variable was the adult
obesity rate. The 23 risk factors were categorized into four
domains of the social ecological model including
biological/behavioral factor, socioeconomic status, food
environment, and physical environment.
Of the 23 risk factors related to adult obesity, the top eight
significant risk factors with high normalized importance were
identified including physical inactivity, natural amenity, percent
of households receiving SNAP benefits, and percent of all
restaurants being fast food. The study results were consistent
with those in the literature.
The study showed that adult obesity rate was influenced by
biological/behavioral factor, socioeconomic status, food
environment, and physical environment embedded in the social
ecological theory. 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. Full Text
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