Introduction
Obesity and diabetes rates across the United States are increasing at an alarming rate [3]. Americans are falling susceptible to fast food branding and convenience, and, in an effort to combat the rising rates, the US government has started a plethora of health initiatives to help people lead healthier lives [4]. As optimistic as these programs are, health issues in the US are still on the rise.
Machine learning can enable us to predict a person’s risk for developing obesity or diabetes later in life, as well as allow us to analyze the factors that have the highest influence on said risk. The United States Department of Agriculture publishes food environment data for counties around the country, providing a means for developing our models and comparing relevant features.
Through our work, we aimed to help identify the features that most highly correspond to higher obesity and diabetes rates, in the hope to provide Americans with insight about their health risks and food decisions. Here, we propose that there is a strong relationship between financial status and obesity and diabetes rates and that there may be a bigger underlying problem to the increasing health problems in the US.
Machine learning can enable us to predict a person’s risk for developing obesity or diabetes later in life, as well as allow us to analyze the factors that have the highest influence on said risk. The United States Department of Agriculture publishes food environment data for counties around the country, providing a means for developing our models and comparing relevant features.
Through our work, we aimed to help identify the features that most highly correspond to higher obesity and diabetes rates, in the hope to provide Americans with insight about their health risks and food decisions. Here, we propose that there is a strong relationship between financial status and obesity and diabetes rates and that there may be a bigger underlying problem to the increasing health problems in the US.