A Machine-Learning and Demographic Approach to Predicting Life-Expectancy at the County Level

Abstract

By Chris Poptic, Biological Sciences

Advisor: Jason Lu

Presentation ID: 210

Abstract: There exists wide variation in life-expectancy and rates of cardiovascular disease in the US at the region, state, and county levels. We build a predictive model using machine learning to identify the demographic, population, and community features that are best predictors of rates of CVD including race, sex, median income, physical activity levels, access to healthcare, population density, dietary habits, and smoking habits.