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COVID-19 patient health prediction using boosted random forest algorithm

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成果类型:
期刊论文
作者:
Iwendi, Celestine*;Bashir, Ali Kashif;Peshkar, Atharva;Sujatha, R.;Chatterjee, Jyotir Moy;...
通讯作者:
Iwendi, Celestine;Jo, Ohyun
作者机构:
[Iwendi, Celestine] BCC Cent South Univ Forestry & Technol, Changsha, Peoples R China.
[Bashir, Ali Kashif] Manchester Metropolitan Univ, Dept Comp & Math, Manchester, Lancs, England.
[Peshkar, Atharva] GH Raisoni Coll Engn, Dept Informat Technol, Nagpur, Maharashtra, India.
[Sujatha, R.] VIT Univ, Sch Informat Technol & Engn, Vellore, Tamil Nadu, India.
[Chatterjee, Jyotir Moy] Lord Buddha Educ Fdn, Dept Informat Technol, Kathmandu, Nepal.
通讯机构:
[Iwendi, Celestine] B
[Jo, Ohyun] C
BCC Cent South Univ Forestry & Technol, Changsha, Peoples R China.
Chungbuk Natl Univ, Coll Elect & Comp Engn, Dept Comp Sci, Cheongju, South Korea.
语种:
英文
关键词:
Boosting;COVID-19;Healthcare analytics;Infection;Patient data;Random forest classification
期刊:
Frontiers in Public Health
ISSN:
2296-2565
年:
2020
卷:
8
页码:
562169
基金类别:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: this work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2018R1C1B5045013).
机构署名:
本校为第一且通讯机构
摘要:
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 20...

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