Developing an artificial neural network for detecting COVID-19 disease
BACKGROUND: From December 2019, atypical pneumonia termed COVID-19 has been increasing exponentially across the world. It poses a great threat and challenge to world health and the economy. Medical specialists face uncertainty in making decisions based on their judgment for COVID-19. Thus, this stud...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2022-01-01
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| Series: | Journal of Education and Health Promotion |
| Subjects: | |
| Online Access: | http://www.jehp.net/article.asp?issn=2277-9531;year=2022;volume=11;issue=1;spage=2;epage=2;aulast=Shanbehzadeh |
| _version_ | 1818279284440039424 |
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| author | Mostafa Shanbehzadeh Raoof Nopour Hadi Kazemi-Arpanahi |
| author_facet | Mostafa Shanbehzadeh Raoof Nopour Hadi Kazemi-Arpanahi |
| author_sort | Mostafa Shanbehzadeh |
| collection | DOAJ |
| description | BACKGROUND: From December 2019, atypical pneumonia termed COVID-19 has been increasing exponentially across the world. It poses a great threat and challenge to world health and the economy. Medical specialists face uncertainty in making decisions based on their judgment for COVID-19. Thus, this study aimed to establish an intelligent model based on artificial neural networks (ANNs) for diagnosing COVID-19.
MATERIALS AND METHODS: Using a single-center registry, we studied the records of 250 confirmed COVID-19 and 150 negative cases from February 9, 2020, to October 20, 2020. The correlation coefficient technique was used to determine the most significant variables of the ANN model. The variables at P < 0.05 were used for model construction. We applied the back-propagation technique for training a neural network on the dataset. After comparing different neural network configurations, the best configuration of ANN was acquired, then its strength has been evaluated.
RESULTS: After the feature selection process, a total of 18 variables were determined as the most relevant predictors for developing the ANN models. The results indicated that two nested loops' architecture of 9-10-15-2 (10 and 15 neurons used in layer 1 and layer 2, respectively) with the area under the curve of 0.982, the sensitivity of 96.4%, specificity of 90.6%, and accuracy of 94% was introduced as the best configuration model for COVID-19 diagnosis.
CONCLUSION: The proposed ANN-based clinical decision support system could be considered as a suitable computational technique for the frontline practitioner in early detection, effective intervention, and possibly a reduction of mortality in patients with COVID-19. |
| first_indexed | 2024-12-12T23:30:53Z |
| format | Article |
| id | doaj.art-89c5933692024b87b890261eeaabedd2 |
| institution | Directory Open Access Journal |
| issn | 2277-9531 |
| language | English |
| last_indexed | 2024-12-12T23:30:53Z |
| publishDate | 2022-01-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Journal of Education and Health Promotion |
| spelling | doaj.art-89c5933692024b87b890261eeaabedd22022-12-22T00:07:47ZengWolters Kluwer Medknow PublicationsJournal of Education and Health Promotion2277-95312022-01-011112210.4103/jehp.jehp_387_21Developing an artificial neural network for detecting COVID-19 diseaseMostafa ShanbehzadehRaoof NopourHadi Kazemi-ArpanahiBACKGROUND: From December 2019, atypical pneumonia termed COVID-19 has been increasing exponentially across the world. It poses a great threat and challenge to world health and the economy. Medical specialists face uncertainty in making decisions based on their judgment for COVID-19. Thus, this study aimed to establish an intelligent model based on artificial neural networks (ANNs) for diagnosing COVID-19. MATERIALS AND METHODS: Using a single-center registry, we studied the records of 250 confirmed COVID-19 and 150 negative cases from February 9, 2020, to October 20, 2020. The correlation coefficient technique was used to determine the most significant variables of the ANN model. The variables at P < 0.05 were used for model construction. We applied the back-propagation technique for training a neural network on the dataset. After comparing different neural network configurations, the best configuration of ANN was acquired, then its strength has been evaluated. RESULTS: After the feature selection process, a total of 18 variables were determined as the most relevant predictors for developing the ANN models. The results indicated that two nested loops' architecture of 9-10-15-2 (10 and 15 neurons used in layer 1 and layer 2, respectively) with the area under the curve of 0.982, the sensitivity of 96.4%, specificity of 90.6%, and accuracy of 94% was introduced as the best configuration model for COVID-19 diagnosis. CONCLUSION: The proposed ANN-based clinical decision support system could be considered as a suitable computational technique for the frontline practitioner in early detection, effective intervention, and possibly a reduction of mortality in patients with COVID-19.http://www.jehp.net/article.asp?issn=2277-9531;year=2022;volume=11;issue=1;spage=2;epage=2;aulast=Shanbehzadehartificial intelligentcoronaviruscovid-19decision support systemsmachine learningneural network |
| spellingShingle | Mostafa Shanbehzadeh Raoof Nopour Hadi Kazemi-Arpanahi Developing an artificial neural network for detecting COVID-19 disease Journal of Education and Health Promotion artificial intelligent coronavirus covid-19 decision support systems machine learning neural network |
| title | Developing an artificial neural network for detecting COVID-19 disease |
| title_full | Developing an artificial neural network for detecting COVID-19 disease |
| title_fullStr | Developing an artificial neural network for detecting COVID-19 disease |
| title_full_unstemmed | Developing an artificial neural network for detecting COVID-19 disease |
| title_short | Developing an artificial neural network for detecting COVID-19 disease |
| title_sort | developing an artificial neural network for detecting covid 19 disease |
| topic | artificial intelligent coronavirus covid-19 decision support systems machine learning neural network |
| url | http://www.jehp.net/article.asp?issn=2277-9531;year=2022;volume=11;issue=1;spage=2;epage=2;aulast=Shanbehzadeh |
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