Date of Award
7-19-2021
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Department of Mathematics
First Advisor
Olcay Akman
Abstract
The abundance of type and quantity of available data in the healthcare field has led many to utilize machine learning approaches to keep up with this influx of data. Data pertaining to COVID-19 is an area of recent interest. The widespread influence of the virus across the United States creates an obvious need to identify groups of individuals that are at an increased risk of mortality from the virus. We propose a so-called clustered random forest approach to predict COVID-19 patient mortality. We use this approach to examine the hidden heterogeneity of patient frailty by examining demographic information for COVID-19 patients. We find that our clustered random forest approach attains predictive performance comparable to other published methods. We also find that follow-up analysis with neural network modeling and k-means clustering provide insight into the type and magnitude of mortality risks associated with COVID-19.
Recommended Citation
Cornelius, Erwin, "Covid-19 Mortality Prediction Using Machine Learning-Integrated Random Forest Algorithm under Varying Patient Frailty" (2021). Theses and Dissertations. 1485.
https://ir.library.illinoisstate.edu/etd/1485
DOI
https://doi.org/10.30707/ETD2021.20220215070316631470.999996
Page Count
50
Comments
Imported from Cornelius_ilstu_0092N_12018.pdf