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...
Main Authors: | Erwin Cornelius, Olcay Akman, Dan Hrozencik |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-08-01
|
Series: | Mathematics |
Subjects: | |
Online Access: | https://www.mdpi.com/2227-7390/9/17/2043 |
Similar Items
-
Javanese Gender Speech Recognition Based on Machine Learning Using Random Forest and Neural Network
by: Kristiawan Nugroho
Published: (2020-02-01) -
Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images
by: Edwin Raczko, et al.
Published: (2017-01-01) -
Predicting Highway Construction Costs: Comparison of the Performance of Random Forest, Neural Network and Support Vector Machine Models
by: Meseret Meharie, et al.
Published: (2020-04-01) -
Fault Detection in Wireless Sensor Networks through the Random Forest Classifier
by: Zainib Noshad, et al.
Published: (2019-04-01) -
PERFORMANCE COMPARISON OF MACHINE LEARNING ALGORITHMS FOR PREDICTIVE MAINTENANCE
by: Jakub Gęca
Published: (2020-09-01)