COVID-19, a disease starting from December 2019, spreads from person to person through contact, and has symptoms of cough, fever, muscle pain, etc. The diagnosis is usually done by polymerase chain reaction (PCR) test which collects samples from the nasopharyngeal area. Today, machine learning or deep learning is used to analyze data such as confirmed cases or mortality, differentiate x-ray images of COVID-19 patients and others. Not many of the researches completed before predicted important features that influence COVID-19. Therefore, we mainly address the influence of related features. Our data includes demographic, geographic, and severity information in Toronto. The experiment was developed in this order: data import, label encoding, correlation matrix, train-test split, min-max normalization, machine learning models, gridsearchcv, and feature importance. We applied a boosting algorithm and light gradient boosting machine to increase accuracy and speed, gridsearchcv, feature importance function to find the importance of the variable and best hyper parameters for models. Among two experiments, the first experiment using a feature-selected model concluded important features such as outbreak associated, FSA, and classification with 88 percent accuracy. The second experiment that did not select features but used entire features resulted in that neighborhood name, FSA, and age group as important features. The accuracy was mostly around 89 percent. The data did not include personal information but mostly geographical information, which might have influenced the result, determining geographical features as key features of infection, and the accuracy. Yet, the model for the experiment has advanced computation speed, less memory usage, and showed impressive performance.
| Published in | American Journal of Mathematical and Computer Modelling (Volume 6, Issue 3) |
| DOI | 10.11648/j.ajmcm.20210603.11 |
| Page(s) | 43-49 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
COVID-19, Machine Learning, Random Forest, LGBM, Artificial Intelligence
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APA Style
Yein Choi. (2021). Predicting Important Features That Influence COVID-19 Infection Through Light Gradient Boosting Machine: Case of Toronto. American Journal of Mathematical and Computer Modelling, 6(3), 43-49. https://doi.org/10.11648/j.ajmcm.20210603.11
ACS Style
Yein Choi. Predicting Important Features That Influence COVID-19 Infection Through Light Gradient Boosting Machine: Case of Toronto. Am. J. Math. Comput. Model. 2021, 6(3), 43-49. doi: 10.11648/j.ajmcm.20210603.11
AMA Style
Yein Choi. Predicting Important Features That Influence COVID-19 Infection Through Light Gradient Boosting Machine: Case of Toronto. Am J Math Comput Model. 2021;6(3):43-49. doi: 10.11648/j.ajmcm.20210603.11
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Download@article{10.11648/j.ajmcm.20210603.11,
author = {Yein Choi},
title = {Predicting Important Features That Influence COVID-19 Infection Through Light Gradient Boosting Machine: Case of Toronto},
journal = {American Journal of Mathematical and Computer Modelling},
volume = {6},
number = {3},
pages = {43-49},
doi = {10.11648/j.ajmcm.20210603.11},
url = {https://doi.org/10.11648/j.ajmcm.20210603.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmcm.20210603.11},
abstract = {COVID-19, a disease starting from December 2019, spreads from person to person through contact, and has symptoms of cough, fever, muscle pain, etc. The diagnosis is usually done by polymerase chain reaction (PCR) test which collects samples from the nasopharyngeal area. Today, machine learning or deep learning is used to analyze data such as confirmed cases or mortality, differentiate x-ray images of COVID-19 patients and others. Not many of the researches completed before predicted important features that influence COVID-19. Therefore, we mainly address the influence of related features. Our data includes demographic, geographic, and severity information in Toronto. The experiment was developed in this order: data import, label encoding, correlation matrix, train-test split, min-max normalization, machine learning models, gridsearchcv, and feature importance. We applied a boosting algorithm and light gradient boosting machine to increase accuracy and speed, gridsearchcv, feature importance function to find the importance of the variable and best hyper parameters for models. Among two experiments, the first experiment using a feature-selected model concluded important features such as outbreak associated, FSA, and classification with 88 percent accuracy. The second experiment that did not select features but used entire features resulted in that neighborhood name, FSA, and age group as important features. The accuracy was mostly around 89 percent. The data did not include personal information but mostly geographical information, which might have influenced the result, determining geographical features as key features of infection, and the accuracy. Yet, the model for the experiment has advanced computation speed, less memory usage, and showed impressive performance.},
year = {2021}
}
TY - JOUR T1 - Predicting Important Features That Influence COVID-19 Infection Through Light Gradient Boosting Machine: Case of Toronto AU - Yein Choi Y1 - 2021/07/13 PY - 2021 N1 - https://doi.org/10.11648/j.ajmcm.20210603.11 DO - 10.11648/j.ajmcm.20210603.11 T2 - American Journal of Mathematical and Computer Modelling JF - American Journal of Mathematical and Computer Modelling JO - American Journal of Mathematical and Computer Modelling SP - 43 EP - 49 PB - Science Publishing Group SN - 2578-8280 UR - https://doi.org/10.11648/j.ajmcm.20210603.11 AB - COVID-19, a disease starting from December 2019, spreads from person to person through contact, and has symptoms of cough, fever, muscle pain, etc. The diagnosis is usually done by polymerase chain reaction (PCR) test which collects samples from the nasopharyngeal area. Today, machine learning or deep learning is used to analyze data such as confirmed cases or mortality, differentiate x-ray images of COVID-19 patients and others. Not many of the researches completed before predicted important features that influence COVID-19. Therefore, we mainly address the influence of related features. Our data includes demographic, geographic, and severity information in Toronto. The experiment was developed in this order: data import, label encoding, correlation matrix, train-test split, min-max normalization, machine learning models, gridsearchcv, and feature importance. We applied a boosting algorithm and light gradient boosting machine to increase accuracy and speed, gridsearchcv, feature importance function to find the importance of the variable and best hyper parameters for models. Among two experiments, the first experiment using a feature-selected model concluded important features such as outbreak associated, FSA, and classification with 88 percent accuracy. The second experiment that did not select features but used entire features resulted in that neighborhood name, FSA, and age group as important features. The accuracy was mostly around 89 percent. The data did not include personal information but mostly geographical information, which might have influenced the result, determining geographical features as key features of infection, and the accuracy. Yet, the model for the experiment has advanced computation speed, less memory usage, and showed impressive performance. VL - 6 IS - 3 ER -
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