LogNNet model as a fast, simple and economical AI instrument in the diagnosis and prognosis of COVID-19
Rapid and effective detection of the diagnosis and prognosis of COVID-19 disease is important in terms of reducing the mortality of the disease and reducing the pressure on health systems. Methods such as PCR testing and computed tomography used for this purpose in current health systems are costly,...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | MethodsX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016123001942 |
| _version_ | 1797796376784928768 |
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| author | Mehmet Tahir Huyut Andrei Velichko |
| author_facet | Mehmet Tahir Huyut Andrei Velichko |
| author_sort | Mehmet Tahir Huyut |
| collection | DOAJ |
| description | Rapid and effective detection of the diagnosis and prognosis of COVID-19 disease is important in terms of reducing the mortality of the disease and reducing the pressure on health systems. Methods such as PCR testing and computed tomography used for this purpose in current health systems are costly, require an expert team and take time. This study offers a fast, economical and reliable approach for the early diagnosis and prognosis of infectious diseases, especially COVID-19. For this purpose, characteristics of a large population of COVID-19 patients were determined (51 different routine blood values) and calibrated. In order to determine the diagnosis and prognosis of the disease, the calibrated features were run with the LogNNet model. LogNNet has a simple algorithm and performance indicators comparable to the most efficient algorithms available.This approach pointed out that routine blood values contain important information, especially in the detection of COVID-19, and showed that the LogNNet model can be used as an economical, safe and fast alternative tool in the diagnosis of this disease. - In the LogNNet feedforward neural network, a feature vector is passed through a specially designed reservoir matrix and transformed into a new feature vector of a different size, increasing the classification accuracy. - The presented network architecture can achieve 80%−99% classification accuracy using a range of weightings on devices with a total memory size of 1 to 29 kB constrained. - Due to the chaotic mapping procedures, the RAM usage in the LogNNet neural network processing process is greatly reduced. Hence, optimization of chaotic map parameters has an important function in LogNNet neural network application. |
| first_indexed | 2024-03-13T03:32:09Z |
| format | Article |
| id | doaj.art-30e3bd715d3947638e6d3f90979a4652 |
| institution | Directory Open Access Journal |
| issn | 2215-0161 |
| language | English |
| last_indexed | 2024-03-13T03:32:09Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj.art-30e3bd715d3947638e6d3f90979a46522023-06-24T05:17:41ZengElsevierMethodsX2215-01612023-01-0110102194LogNNet model as a fast, simple and economical AI instrument in the diagnosis and prognosis of COVID-19Mehmet Tahir Huyut0Andrei Velichko1Department of Biostatistics and Medical Informatics, Faculty of Medicine, Erzincan Binali Yıldırım University, 24000 Erzincan, Turkey; Corresponding author.Institute of Physics and Technology, Petrozavodsk State University, 33 Lenin Str., 185910 Petrozavodsk, RussiaRapid and effective detection of the diagnosis and prognosis of COVID-19 disease is important in terms of reducing the mortality of the disease and reducing the pressure on health systems. Methods such as PCR testing and computed tomography used for this purpose in current health systems are costly, require an expert team and take time. This study offers a fast, economical and reliable approach for the early diagnosis and prognosis of infectious diseases, especially COVID-19. For this purpose, characteristics of a large population of COVID-19 patients were determined (51 different routine blood values) and calibrated. In order to determine the diagnosis and prognosis of the disease, the calibrated features were run with the LogNNet model. LogNNet has a simple algorithm and performance indicators comparable to the most efficient algorithms available.This approach pointed out that routine blood values contain important information, especially in the detection of COVID-19, and showed that the LogNNet model can be used as an economical, safe and fast alternative tool in the diagnosis of this disease. - In the LogNNet feedforward neural network, a feature vector is passed through a specially designed reservoir matrix and transformed into a new feature vector of a different size, increasing the classification accuracy. - The presented network architecture can achieve 80%−99% classification accuracy using a range of weightings on devices with a total memory size of 1 to 29 kB constrained. - Due to the chaotic mapping procedures, the RAM usage in the LogNNet neural network processing process is greatly reduced. Hence, optimization of chaotic map parameters has an important function in LogNNet neural network application.http://www.sciencedirect.com/science/article/pii/S2215016123001942Detection of diagnosis and prognosis of COVID-19 with LogNNet deep neural network architecture |
| spellingShingle | Mehmet Tahir Huyut Andrei Velichko LogNNet model as a fast, simple and economical AI instrument in the diagnosis and prognosis of COVID-19 MethodsX Detection of diagnosis and prognosis of COVID-19 with LogNNet deep neural network architecture |
| title | LogNNet model as a fast, simple and economical AI instrument in the diagnosis and prognosis of COVID-19 |
| title_full | LogNNet model as a fast, simple and economical AI instrument in the diagnosis and prognosis of COVID-19 |
| title_fullStr | LogNNet model as a fast, simple and economical AI instrument in the diagnosis and prognosis of COVID-19 |
| title_full_unstemmed | LogNNet model as a fast, simple and economical AI instrument in the diagnosis and prognosis of COVID-19 |
| title_short | LogNNet model as a fast, simple and economical AI instrument in the diagnosis and prognosis of COVID-19 |
| title_sort | lognnet model as a fast simple and economical ai instrument in the diagnosis and prognosis of covid 19 |
| topic | Detection of diagnosis and prognosis of COVID-19 with LogNNet deep neural network architecture |
| url | http://www.sciencedirect.com/science/article/pii/S2215016123001942 |
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