Demographic Data-driven Deprivation Index for Predicting Chronic Diseases

Researchers have worked on modeling and predicting the likelihood of developing chronic diseases, such as diabetes and high blood pressure, using medical data (e.g., heart-rate, blood sugar). However, many of these diseases demonstrate strong links with demographics and socio-economic status (e.g., race, gender, income). It is also less time-consuming to retrieve demographic and socio-economic data, some of which are publicly available through the U.S. Census Bureau, than to conduct medical tests. Hence, such data can give a quicker estimate of the susceptibility of a person to a chronic disease.In this work, we study the effect of using medical vs. demographic data for modeling and predicting two chronic diseases: diabetes and high blood pressure. We proposed an updated deprivation index to build disease models that consider demographic data. Our results indicate demographic data are as good or better indicators for predicting chronic diseases.