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Comparative analysis of feature selection techniques for COVID-19 dataset.

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Title: Comparative analysis of feature selection techniques for COVID-19 dataset.
Authors: Mohtasham, Farideh1 f-mohtasham@sbmu.ac.ir, Pourhoseingholi, MohamadAmin2, Hashemi Nazari, Seyed Saeed3, Kavousi, Kaveh4 kkavousi@ut.ac.ir, Zali, Mohammad Reza1
Source: Scientific Reports. 8/15/2024, Vol. 14 Issue 1, p1-20. 20p.
Subject Terms: *FEATURE selection, *RANDOM forest algorithms, *EARLY diagnosis, *OXYGEN saturation, *KIDNEY physiology, *MACHINE learning
Geographic Terms: IRAN
Abstract: In the context of early disease detection, machine learning (ML) has emerged as a vital tool. Feature selection (FS) algorithms play a crucial role in ensuring the accuracy of predictive models by identifying the most influential variables. This study, focusing on a retrospective cohort of 4778 COVID-19 patients from Iran, explores the performance of various FS methods, including filter, embedded, and hybrid approaches, in predicting mortality outcomes. The researchers leveraged 115 routine clinical, laboratory, and demographic features and employed 13 ML models to assess the effectiveness of these FS methods based on classification accuracy, predictive accuracy, and statistical tests. The results indicate that a Hybrid Boruta-VI model combined with the Random Forest algorithm demonstrated superior performance, achieving an accuracy of 0.89, an F1 score of 0.76, and an AUC value of 0.95 on test data. Key variables identified as important predictors of adverse outcomes include age, oxygen saturation levels, albumin levels, neutrophil counts, platelet levels, and markers of kidney function. These findings highlight the potential of advanced FS techniques and ML models in enhancing early disease detection and informing clinical decision-making. [ABSTRACT FROM AUTHOR]
Copyright of Scientific Reports is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Comparative analysis of feature selection techniques for COVID-19 dataset.
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  Data: <searchLink fieldCode="AR" term="%22Mohtasham%2C+Farideh%22">Mohtasham, Farideh</searchLink><relatesTo>1</relatesTo><i> f-mohtasham@sbmu.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Pourhoseingholi%2C+MohamadAmin%22">Pourhoseingholi, MohamadAmin</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Hashemi+Nazari%2C+Seyed+Saeed%22">Hashemi Nazari, Seyed Saeed</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Kavousi%2C+Kaveh%22">Kavousi, Kaveh</searchLink><relatesTo>4</relatesTo><i> kkavousi@ut.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Zali%2C+Mohammad+Reza%22">Zali, Mohammad Reza</searchLink><relatesTo>1</relatesTo>
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  Data: *<searchLink fieldCode="DE" term="%22FEATURE+selection%22">FEATURE selection</searchLink><br />*<searchLink fieldCode="DE" term="%22RANDOM+forest+algorithms%22">RANDOM forest algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22EARLY+diagnosis%22">EARLY diagnosis</searchLink><br />*<searchLink fieldCode="DE" term="%22OXYGEN+saturation%22">OXYGEN saturation</searchLink><br />*<searchLink fieldCode="DE" term="%22KIDNEY+physiology%22">KIDNEY physiology</searchLink><br />*<searchLink fieldCode="DE" term="%22MACHINE+learning%22">MACHINE learning</searchLink>
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  Data: In the context of early disease detection, machine learning (ML) has emerged as a vital tool. Feature selection (FS) algorithms play a crucial role in ensuring the accuracy of predictive models by identifying the most influential variables. This study, focusing on a retrospective cohort of 4778 COVID-19 patients from Iran, explores the performance of various FS methods, including filter, embedded, and hybrid approaches, in predicting mortality outcomes. The researchers leveraged 115 routine clinical, laboratory, and demographic features and employed 13 ML models to assess the effectiveness of these FS methods based on classification accuracy, predictive accuracy, and statistical tests. The results indicate that a Hybrid Boruta-VI model combined with the Random Forest algorithm demonstrated superior performance, achieving an accuracy of 0.89, an F1 score of 0.76, and an AUC value of 0.95 on test data. Key variables identified as important predictors of adverse outcomes include age, oxygen saturation levels, albumin levels, neutrophil counts, platelet levels, and markers of kidney function. These findings highlight the potential of advanced FS techniques and ML models in enhancing early disease detection and informing clinical decision-making. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Scientific Reports is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: 8/15/2024
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