Incorporating Feature Selection Methods into Machine Learning-Based Covid-19 Diagnosis
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, t...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2022-06-01
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| Series: | Applied Computer Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/acss-2022-0002 |
| _version_ | 1798042733588250624 |
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| author | Danacı Çağla Tuncer Seda Arslan |
| author_facet | Danacı Çağla Tuncer Seda Arslan |
| author_sort | Danacı Çağla |
| collection | DOAJ |
| description | 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 %. |
| first_indexed | 2024-04-11T22:39:32Z |
| format | Article |
| id | doaj.art-904f1cd669b34e60a2283d72dcdff126 |
| institution | Directory Open Access Journal |
| issn | 2255-8691 |
| language | English |
| last_indexed | 2024-04-11T22:39:32Z |
| publishDate | 2022-06-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Computer Systems |
| spelling | doaj.art-904f1cd669b34e60a2283d72dcdff1262022-12-22T03:59:02ZengSciendoApplied Computer Systems2255-86912022-06-01271131810.2478/acss-2022-0002Incorporating Feature Selection Methods into Machine Learning-Based Covid-19 DiagnosisDanacı Çağla0Tuncer Seda Arslan1Fırat University, Department of Software Engineering, Elazığ, TurkeyFırat University, Department of Software Engineering, Elazığ, TurkeyThe 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 %.https://doi.org/10.2478/acss-2022-0002classificationcovid-19feature selectionmachine learning |
| spellingShingle | Danacı Çağla Tuncer Seda Arslan Incorporating Feature Selection Methods into Machine Learning-Based Covid-19 Diagnosis Applied Computer Systems classification covid-19 feature selection machine learning |
| title | Incorporating Feature Selection Methods into Machine Learning-Based Covid-19 Diagnosis |
| title_full | Incorporating Feature Selection Methods into Machine Learning-Based Covid-19 Diagnosis |
| title_fullStr | Incorporating Feature Selection Methods into Machine Learning-Based Covid-19 Diagnosis |
| title_full_unstemmed | Incorporating Feature Selection Methods into Machine Learning-Based Covid-19 Diagnosis |
| title_short | Incorporating Feature Selection Methods into Machine Learning-Based Covid-19 Diagnosis |
| title_sort | incorporating feature selection methods into machine learning based covid 19 diagnosis |
| topic | classification covid-19 feature selection machine learning |
| url | https://doi.org/10.2478/acss-2022-0002 |
| work_keys_str_mv | AT danacıcagla incorporatingfeatureselectionmethodsintomachinelearningbasedcovid19diagnosis AT tuncersedaarslan incorporatingfeatureselectionmethodsintomachinelearningbasedcovid19diagnosis |