Automated detection of coronavirus disease (COVID-19) by using data-mining techniques: a brief report
Background: The clinical field has vast sick data that has not been analyzed. Discovering a way to analyze this raw data and turn it into an information treasure can save many lives. Using data mining methods is an efficient way to analyze this large amount of raw data. It can predict the future wit...
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| Format: | Article |
| Language: | fas |
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Tehran University of Medical Sciences
2021-12-01
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| Series: | Tehran University Medical Journal |
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| Online Access: | http://tumj.tums.ac.ir/article-1-11474-en.html |
| _version_ | 1798034395723988992 |
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| author | Seyed Ali Akbar Arabzadeh Vahid Jamshidi Masoud Saeed Rostam Yazdani Mahdieh Jamshidi |
| author_facet | Seyed Ali Akbar Arabzadeh Vahid Jamshidi Masoud Saeed Rostam Yazdani Mahdieh Jamshidi |
| author_sort | Seyed Ali Akbar Arabzadeh |
| collection | DOAJ |
| description | Background: The clinical field has vast sick data that has not been analyzed. Discovering a way to analyze this raw data and turn it into an information treasure can save many lives. Using data mining methods is an efficient way to analyze this large amount of raw data. It can predict the future with accurate knowledge of the past, providing new insights into disease diagnosis and prevention. Several data mining methods exist but finding a suitable one is very important. Today, coronavirus disease (COVID-19) has become one of the causing deadly diseases in the world. The early diagnosis of pandemic coronavirus disease has a significant impact in preventing death. This study aims to extract the key indications of the disease and find the best data mining methods that enhance the accuracy of coronavirus disease diagnosis.
Methods: In this study, to obtain high accuracy in diagnosing COVID-19 disease, a complete and effective workflow over data mining methods was proposed, which includes these steps: data pre-analyzing, indication selection, model creation, the measure of performance, and display of results. Data and related indications of patients with COVID-19 were collected from Kerman Afzalipour Hospital and Rafsanjan, Ali Ebn Abi Taleb Hospital. Prediction structures were made and tested via different combinations of the disease indications and seven data mining methods. To discover the best key indications, three criteria including accuracy, validation and F-value were applied and to discover the best data mining methods, accuracy and validation criteria were considered. For each data mining method, the criteria were measured independently and all results were reported for analysis. Finally, the best key indications and data mining methods that can diagnose COVID-19 disease with high accuracy were extracted.
Results: 9 key indications and 3 data mining methods were obtained. Experimental results show that the discovered key indications and the best-operating data mining method (i.e. SVM) attain an accuracy of 83.19% for the diagnosis of coronavirus disease.
Conclusion: Due to key indications and data mining methods obtained from this study, it is possible to use this method to diagnose coronavirus disease in different people of different clinical indications with high accuracy. |
| first_indexed | 2024-04-11T20:43:47Z |
| format | Article |
| id | doaj.art-b52231ad63fd41539a3dd65656c2b8de |
| institution | Directory Open Access Journal |
| issn | 1683-1764 1735-7322 |
| language | fas |
| last_indexed | 2024-04-11T20:43:47Z |
| publishDate | 2021-12-01 |
| publisher | Tehran University of Medical Sciences |
| record_format | Article |
| series | Tehran University Medical Journal |
| spelling | doaj.art-b52231ad63fd41539a3dd65656c2b8de2022-12-22T04:04:06ZfasTehran University of Medical SciencesTehran University Medical Journal1683-17641735-73222021-12-017910822830Automated detection of coronavirus disease (COVID-19) by using data-mining techniques: a brief reportSeyed Ali Akbar Arabzadeh0Vahid Jamshidi1Masoud Saeed2Rostam Yazdani3Mahdieh Jamshidi4 Department of Computer Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran. Department of Computer Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran. Department of Computer Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran. Department of Internal Medicine, Faculty of Medicine, Kerman University of Medical Sciences, Kerman, Iran. Department of Internal Medicine, Faculty of Medicine, Rafsanjan University of Medical Sciences, Rafsanjan, Iran. Background: The clinical field has vast sick data that has not been analyzed. Discovering a way to analyze this raw data and turn it into an information treasure can save many lives. Using data mining methods is an efficient way to analyze this large amount of raw data. It can predict the future with accurate knowledge of the past, providing new insights into disease diagnosis and prevention. Several data mining methods exist but finding a suitable one is very important. Today, coronavirus disease (COVID-19) has become one of the causing deadly diseases in the world. The early diagnosis of pandemic coronavirus disease has a significant impact in preventing death. This study aims to extract the key indications of the disease and find the best data mining methods that enhance the accuracy of coronavirus disease diagnosis. Methods: In this study, to obtain high accuracy in diagnosing COVID-19 disease, a complete and effective workflow over data mining methods was proposed, which includes these steps: data pre-analyzing, indication selection, model creation, the measure of performance, and display of results. Data and related indications of patients with COVID-19 were collected from Kerman Afzalipour Hospital and Rafsanjan, Ali Ebn Abi Taleb Hospital. Prediction structures were made and tested via different combinations of the disease indications and seven data mining methods. To discover the best key indications, three criteria including accuracy, validation and F-value were applied and to discover the best data mining methods, accuracy and validation criteria were considered. For each data mining method, the criteria were measured independently and all results were reported for analysis. Finally, the best key indications and data mining methods that can diagnose COVID-19 disease with high accuracy were extracted. Results: 9 key indications and 3 data mining methods were obtained. Experimental results show that the discovered key indications and the best-operating data mining method (i.e. SVM) attain an accuracy of 83.19% for the diagnosis of coronavirus disease. Conclusion: Due to key indications and data mining methods obtained from this study, it is possible to use this method to diagnose coronavirus disease in different people of different clinical indications with high accuracy.http://tumj.tums.ac.ir/article-1-11474-en.htmldata miningdiagnosisclinical symptomscoronaviruscovid-19pandemics. |
| spellingShingle | Seyed Ali Akbar Arabzadeh Vahid Jamshidi Masoud Saeed Rostam Yazdani Mahdieh Jamshidi Automated detection of coronavirus disease (COVID-19) by using data-mining techniques: a brief report Tehran University Medical Journal data mining diagnosis clinical symptoms coronavirus covid-19 pandemics. |
| title | Automated detection of coronavirus disease (COVID-19)
by using data-mining techniques: a brief report |
| title_full | Automated detection of coronavirus disease (COVID-19)
by using data-mining techniques: a brief report |
| title_fullStr | Automated detection of coronavirus disease (COVID-19)
by using data-mining techniques: a brief report |
| title_full_unstemmed | Automated detection of coronavirus disease (COVID-19)
by using data-mining techniques: a brief report |
| title_short | Automated detection of coronavirus disease (COVID-19)
by using data-mining techniques: a brief report |
| title_sort | automated detection of coronavirus disease covid 19 by using data mining techniques a brief report |
| topic | data mining diagnosis clinical symptoms coronavirus covid-19 pandemics. |
| url | http://tumj.tums.ac.ir/article-1-11474-en.html |
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