A Regionally Generalized Machine Learning Framework Towards Census-Enabled Multi-Factor Non-Communicable Disease Analyses
Year: 2024
Type: conference-paper
Abstract: In recent years, non-communicable diseases, diseases intransmittable through human interaction, have cemented themselves as a leading cause of death globally. These diseases are exacerbated by various social, economic and geographical factors within communities. Despite their resounding consequences on community health, the compilation of medical surveys is both costly and time consuming. This study uses a machine learning approach to predict the population proportion with Asthma, High Blood Pressure, Diabetes, and Poor Mental Health within census tract populations in three states. Through an evaluation of 8 different regression models on their predictive capabilities of disease prevalence, the Cubist model held the strongest results for every disease, achieving considerably low relative mean value errors of 3.849%, 6.077%, 7.656%, 7.866%, and 5.531% towards each of the non-communicable diseases, respectively. Out of the four categories split from 41 input features- Sociodemographic, Economic, Environment/Geographic, and Environmental Justice-Sociodemographic features consistently were the highest contributors. A lightweight model with comparable accuracy to the full feature set ($\leq 0.1\%$difference) was found to require only the 20 most important features from every Cubist model. This study provides insight towards machine learning models' capabilities in enhancing the United States' understanding of domestic epidemiology determinants.
Authors: Kevin Geng, Sudith Thota, Anish Kataria
Institution: Princeton University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4407737053