About: COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm
Creator	Chatterjee, Jyotir Iwendi, Celestine Jo, Ohyun Mishra, Rishita Pasupuleti, Swetha Peshkar, Atharva Pillai, Sofia Ali, Mumtaz Kashif Bashir, Ali Sujatha, R Zaman, Noor
source	Medline; PMC
abstract	Integration of artificial intelligence (AI) techniques in wireless infrastructure, real-time collection, and processing of end-user devices is now in high demand. It is now superlative to use AI to detect and predict pandemics of a colossal nature. The Coronavirus disease 2019 (COVID-19) pandemic, which originated in Wuhan China, has had disastrous effects on the global community and has overburdened advanced healthcare systems throughout the world. Globally; over 4,063,525 confirmed cases and 282,244 deaths have been recorded as of 11th May 2020, according to the European Centre for Disease Prevention and Control agency. However, the current rapid and exponential rise in the number of patients has necessitated efficient and quick prediction of the possible outcome of an infected patient for appropriate treatment using AI techniques. This paper proposes a fine-tuned Random Forest model boosted by the AdaBoost algorithm. The model uses the COVID-19 patient's geographical, travel, health, and demographic data to predict the severity of the case and the possible outcome, recovery, or death. The model has an accuracy of 94% and a F1 Score of 0.86 on the dataset used. The data analysis reveals a positive correlation between patients' gender and deaths, and also indicates that the majority of patients are aged between 20 and 70 years.
has issue date	2020-07-03(xsd:dateTime)
bibo:doi	10.3389/fpubh.2020.00357
bibo:pmid	32719767
has license	cc-by
sha1sum (hex)	c4560af30768217dd37877c28448135fd999e318
schema:url	https://doi.org/10.3389/fpubh.2020.00357
resource representing a document's title	COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm
has PubMed Central identifier	PMC7350612
has PubMed identifier	32719767
schema:publication	Front Public Health
resource representing a document's body	covid:c4560af30768217dd37877c28448135fd999e318#body_text
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