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Light Gradient Boosting with Hyper Parameter Tuning Optimization for COVID-19 Prediction

The 2019 coronavirus disease (COVID-19) caused pandemic and a huge number of deaths in the world. COVID-19 screening is needed to identify suspected positive COVID-19 or not and it can reduce the spread of COVID-19. The polymerase chain reaction (PCR) test for COVID-19 is a test that analyzes the re...

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Main Authors: Ferda, Ernawan, Kartika, Handayani, Mohammad, Fakhreldin, Yagoub, Abbker
Format: Article
Language:English
Published: The Science and Information (SAI) Organization Limited 2022
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/38134/1/Paper_59-Light_Gradient_Boosting_with_Hyper_Parameter_Tuning.pdf
http://umpir.ump.edu.my/id/eprint/38134/
https://doi.org/10.14569/IJACSA.2022.0130859
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spelling my.ump.umpir.381342023-08-01T06:12:39Z http://umpir.ump.edu.my/id/eprint/38134/ Light Gradient Boosting with Hyper Parameter Tuning Optimization for COVID-19 Prediction Ferda, Ernawan Kartika, Handayani Mohammad, Fakhreldin Yagoub, Abbker QA75 Electronic computers. Computer science The 2019 coronavirus disease (COVID-19) caused pandemic and a huge number of deaths in the world. COVID-19 screening is needed to identify suspected positive COVID-19 or not and it can reduce the spread of COVID-19. The polymerase chain reaction (PCR) test for COVID-19 is a test that analyzes the respiratory specimen. The blood test also can be used to show people who have been infected with SARS-CoV-2. In addition, age parameters also contribute to the susceptibility of COVID-19 transmission. This paper presents the extra trees classification with random over-sampling by considering blood and age parameters for COVID-19 screening. This research proposes enhanced preprocessing data by using KNN Imputer to handle large missing values. The experiments evaluated the existing classification methods such as Random Forest, Extra Trees, Ada Boost, Gradient Boosting, and the proposed Light Gradient Boosting with hyperparameter tuning to measure the predictions of patients infected with SARS-CoV-2. The experiments used Albert Einstein Hospital test data in Brazil that consisted of 5,644 sample data from 559 patients with infected SARS-CoV-2. The experimental results show that the proposed scheme achieves an accuracy of about 98,58%, recall of 98,58%, the precision of 98,61%, F1-Score of 98,61%, and AUC of 0,9682. The Science and Information (SAI) Organization Limited 2022 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/38134/1/Paper_59-Light_Gradient_Boosting_with_Hyper_Parameter_Tuning.pdf Ferda, Ernawan and Kartika, Handayani and Mohammad, Fakhreldin and Yagoub, Abbker (2022) Light Gradient Boosting with Hyper Parameter Tuning Optimization for COVID-19 Prediction. International Journal of Advanced Computer Science and Applications (IJACSA), 13 (8). pp. 514-523. ISSN 2156-5570(Online). (Published) https://doi.org/10.14569/IJACSA.2022.0130859 10.14569/IJACSA.2022.0130859
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Ferda, Ernawan
Kartika, Handayani
Mohammad, Fakhreldin
Yagoub, Abbker
Light Gradient Boosting with Hyper Parameter Tuning Optimization for COVID-19 Prediction
description The 2019 coronavirus disease (COVID-19) caused pandemic and a huge number of deaths in the world. COVID-19 screening is needed to identify suspected positive COVID-19 or not and it can reduce the spread of COVID-19. The polymerase chain reaction (PCR) test for COVID-19 is a test that analyzes the respiratory specimen. The blood test also can be used to show people who have been infected with SARS-CoV-2. In addition, age parameters also contribute to the susceptibility of COVID-19 transmission. This paper presents the extra trees classification with random over-sampling by considering blood and age parameters for COVID-19 screening. This research proposes enhanced preprocessing data by using KNN Imputer to handle large missing values. The experiments evaluated the existing classification methods such as Random Forest, Extra Trees, Ada Boost, Gradient Boosting, and the proposed Light Gradient Boosting with hyperparameter tuning to measure the predictions of patients infected with SARS-CoV-2. The experiments used Albert Einstein Hospital test data in Brazil that consisted of 5,644 sample data from 559 patients with infected SARS-CoV-2. The experimental results show that the proposed scheme achieves an accuracy of about 98,58%, recall of 98,58%, the precision of 98,61%, F1-Score of 98,61%, and AUC of 0,9682.
format Article
author Ferda, Ernawan
Kartika, Handayani
Mohammad, Fakhreldin
Yagoub, Abbker
author_facet Ferda, Ernawan
Kartika, Handayani
Mohammad, Fakhreldin
Yagoub, Abbker
author_sort Ferda, Ernawan
title Light Gradient Boosting with Hyper Parameter Tuning Optimization for COVID-19 Prediction
title_short Light Gradient Boosting with Hyper Parameter Tuning Optimization for COVID-19 Prediction
title_full Light Gradient Boosting with Hyper Parameter Tuning Optimization for COVID-19 Prediction
title_fullStr Light Gradient Boosting with Hyper Parameter Tuning Optimization for COVID-19 Prediction
title_full_unstemmed Light Gradient Boosting with Hyper Parameter Tuning Optimization for COVID-19 Prediction
title_sort light gradient boosting with hyper parameter tuning optimization for covid-19 prediction
publisher The Science and Information (SAI) Organization Limited
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/38134/1/Paper_59-Light_Gradient_Boosting_with_Hyper_Parameter_Tuning.pdf
http://umpir.ump.edu.my/id/eprint/38134/
https://doi.org/10.14569/IJACSA.2022.0130859
_version_ 1773543698304335872
score 13.211869

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