Date of Award
2021-05-01
Degree Name
Master of Science
Department
Computer Science
Advisor(s)
Monika Akbar
Abstract
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 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 US Census Bureau, than to carry out medical tests. Hence, demographic 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. demographics data formodelling 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.
Language
en
Provenance
Received from ProQuest
Copyright Date
2021-05
File Size
93 p.
File Format
application/pdf
Rights Holder
OLUGBENGA TEMITOPE IYIOLA
Recommended Citation
Iyiola, Olugbenga Temitope, "On Using Demographic Data With Deprivation Index For Predicting Chronic Diseases" (2021). Open Access Theses & Dissertations. 3276.
https://scholarworks.utep.edu/open_etd/3276