<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The aim of the study is to diagnose Covid-19 by machine learning algorithms using biochemical parameters. In addition to the aim of the study, October selection was performed using 14 different feature selection methods based on the biochemical parameters available to us. As a result of the study, the performance of the algorithms and feature selection methods was evaluated using performance evaluation criteria. The dataset used in the study consists of 100 covid-negative and 121 covid-positive data from a total of 221 patients. The dataset includes 16 biochemical parameters used for the diagnosis of Covid-19. Feature selection methods were used to reduce the number of parameters and perform the classification process. The result of the study shows that the new feature set obtained using feature selection algorithms yields very similar results to the set containing all features. Overall, 5 features obtained from 16 features by feature selection methods yielded the best performance for the K-Nearest Neighbour algorithm with the FSVFS feature selection method of 86.4 %.</p>
</abstract>

The aim of the study is to diagnose Covid-19 by machine learning algorithms using biochemical parameters. In addition to the aim of the study, October selection was performed using 14 different feature selection methods based on the biochemical parameters available to us. As a result of the study, the performance of the algorithms and feature selection methods was evaluated using performance evaluation criteria. The dataset used in the study consists of 100 covid-negative and 121 covid-positive data from a total of 221 patients. The dataset includes 16 biochemical parameters used for the diagnosis of Covid-19. Feature selection methods were used to reduce the number of parameters and perform the classification process. The result of the study shows that the new feature set obtained using feature selection algorithms yields very similar results to the set containing all features. Overall, 5 features obtained from 16 features by feature selection methods yielded the best performance for the K-Nearest Neighbour algorithm with the FSVFS feature selection method of 86.4 %.

Incorporating Feature Selection Methods into Machine Learning-Based Covid-19 Diagnosis

Applied Computer Systems

This work is licensed under the Creative Commons Attribution 4.0 International License.

The aim of the study is to diagnose Covid-19 by machine learning algorithms using biochemical parameters. In addition to the aim of the study, October...

Incorporating Feature Selection Methods into Machine Learning-Based Covid-19 Diagnosis

Online veröffentlicht: 23. Aug. 2022

Seitenbereich: 13 - 18

DOI: https://doi.org/10.2478/acss-2022-0002

Schlüsselwörter
Classification, Covid-19, feature selection, machine learning

© 2022 Çağla Danacı et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Incorporating Feature Selection Methods into Machine Learning-Based Covid-19 Diagnosis

Online veröffentlicht: 23. Aug. 2022

Seitenbereich: 13 - 18

DOI: https://doi.org/10.2478/acss-2022-0002

SchlüsselwörterClassification, Covid-19, feature selection, machine learning

© 2022 Çağla Danacı et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Classification, Covid-19, feature selection, machine learning