Abstract
High blood pressure is a leading cause of cardiovascular disease, with billions of dollars spent in the U.S. every year to treat hypertension. Hypertension has even been classified as an epidemic by some researchers and doctors. As such, it is important to understand who is at risk of developing high blood pressure, so that, hopefully, preventative steps can be taken. Using data acquired from the National Health and Nutrition Examination Survey (NHANES), we developed a regression model to predict systolic blood pressure from demographics and consumer data such as age, gender, education level, marital status, money spent on groceries and restaurants and how far away participants live from a grocery store. We converted the categorical variables we used – marital status, education – into dummy variables. We found that all of the demographics variables used contributed significantly to blood pressure, and that three of the behavior variables did. A logistic regression model was also developed to determine how likely someone with the given attributes is to develop high blood pressure. From the logistic regression, we calculated odds ratios to see if different groups of people were more or less likely to develop high blood pressure relative to each other.
How to Cite
Sharpe, J., (2014) “Predicting Blood Pressure Using Demographics and Consumer Behavior”, Capstone, The UNC Asheville Journal of Undergraduate Scholarship 27(1).
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