COVID-19 Mortality Prediction Using Machine Learning-Integrated Random Forest Algorithm under Varying Patient Frailty

COVID-19 Mortality Prediction Using Machine Learning-Integrated Random Forest Algorithm under Varying Patient Frailty

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 o...

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Bibliographic Details
Main Authors: Erwin Cornelius, Olcay Akman, Dan Hrozencik
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Mathematics
Subjects:
machine learning
random forest
neural network
Online Access:https://www.mdpi.com/2227-7390/9/17/2043
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spelling doaj-5208180e25d74756b9a7b5f6011651012021-09-09T13:52:07ZengMDPI AGMathematics2227-73902021-08-0192043204310.3390/math9172043COVID-19 Mortality Prediction Using Machine Learning-Integrated Random Forest Algorithm under Varying Patient FrailtyErwin Cornelius0Olcay Akman1Dan Hrozencik2Department of Mathematics, Illinois State University, Normal, IL 61701, USADepartment of Mathematics, Illinois State University, Normal, IL 61701, USADepartment of Mathematics, Chicago State University, Chicago, IL 60628, USAThe 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.https://www.mdpi.com/2227-7390/9/17/2043machine learningrandom forestneural network
collection DOAJ
language English
format Article
sources DOAJ
author Erwin Cornelius
Olcay Akman
Dan Hrozencik
spellingShingle Erwin Cornelius
Olcay Akman
Dan Hrozencik
COVID-19 Mortality Prediction Using Machine Learning-Integrated Random Forest Algorithm under Varying Patient Frailty
Mathematics
machine learning
random forest
neural network
author_facet Erwin Cornelius
Olcay Akman
Dan Hrozencik
author_sort Erwin Cornelius
title COVID-19 Mortality Prediction Using Machine Learning-Integrated Random Forest Algorithm under Varying Patient Frailty
title_short COVID-19 Mortality Prediction Using Machine Learning-Integrated Random Forest Algorithm under Varying Patient Frailty
title_full COVID-19 Mortality Prediction Using Machine Learning-Integrated Random Forest Algorithm under Varying Patient Frailty
title_fullStr COVID-19 Mortality Prediction Using Machine Learning-Integrated Random Forest Algorithm under Varying Patient Frailty
title_full_unstemmed COVID-19 Mortality Prediction Using Machine Learning-Integrated Random Forest Algorithm under Varying Patient Frailty
title_sort covid-19 mortality prediction using machine learning-integrated random forest algorithm under varying patient frailty
publisher MDPI AG
series Mathematics
issn 2227-7390
publishDate 2021-08-01
description 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.
topic machine learning
random forest
neural network
url https://www.mdpi.com/2227-7390/9/17/2043
work_keys_str_mv AT erwincornelius covid19mortalitypredictionusingmachinelearningintegratedrandomforestalgorithmundervaryingpatientfrailty
AT olcayakman covid19mortalitypredictionusingmachinelearningintegratedrandomforestalgorithmundervaryingpatientfrailty
AT danhrozencik covid19mortalitypredictionusingmachinelearningintegratedrandomforestalgorithmundervaryingpatientfrailty
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