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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| Format: | Article |
| Language: | English |
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The Science and Information (SAI) Organization Limited
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/38134/1/Paper_59-Light_Gradient_Boosting_with_Hyper_Parameter_Tuning.pdf |
| _version_ | 1825815056027222016 |
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| author | Ferda, Ernawan Kartika, Handayani Mohammad, Fakhreldin Yagoub, Abbker |
| author_facet | Ferda, Ernawan Kartika, Handayani Mohammad, Fakhreldin Yagoub, Abbker |
| author_sort | Ferda, Ernawan |
| collection | UMP |
| 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. |
| first_indexed | 2024-03-06T13:07:45Z |
| format | Article |
| id | UMPir38134 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T13:07:45Z |
| publishDate | 2022 |
| publisher | The Science and Information (SAI) Organization Limited |
| record_format | dspace |
| spelling | UMPir381342023-08-01T06:12:39Z 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 |
| 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 |
| title | 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_short | 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 |
| topic | QA75 Electronic computers. Computer science |
| url | http://umpir.ump.edu.my/id/eprint/38134/1/Paper_59-Light_Gradient_Boosting_with_Hyper_Parameter_Tuning.pdf |
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