On Using Demographic Data with Deprivation Index for Predicting Chronic Diseases

Olugbenga Iyiola, University of Texas at El Paso


Researchers have worked on modeling and predicting the likelihood of developingchronic diseases, such as diabetes and high blood pressure, using medical data (e.g.,heart-rate, blood sugar). However, many of these diseases demonstrate strong linkswith demographics and socio-economic status (e.g., race, gender, income). It is alsoless time-consuming to retrieve demographic and socio-economic data, some of whichare publicly available through US Census Bureau, than to carry out medical tests.Hence, demographic data can give a quicker estimate of the susceptibility of a personto a chronic disease. In this work, we study the effect of using medical vs. demographics data formodelling and predicting two chronic diseases: diabetes and high blood pressure.We proposed an updated deprivation index to build disease models that considerdemographic data. Our results indicate demographic data are as good or betterindicators for predicting chronic diseases.

Subject Area

Computer science|Artificial intelligence|Hispanic American studies

Recommended Citation

Iyiola, Olugbenga, "On Using Demographic Data with Deprivation Index for Predicting Chronic Diseases" (2021). ETD Collection for University of Texas, El Paso. AAI28540802.