. 2023 May 28;13(11):1886.

doi: 10.3390/diagnostics13111886.

A Novel COVID-19 Diagnostic System Using Biosensor Incorporated Artificial Intelligence Technique

Md Mottahir Alam¹, Md Moddassir Alam², Hidayath Mirza³, Nishat Sultana⁴, Nazia Sultana⁵, Amjad Ali Pasha⁶, Asif Irshad Khan⁷, Aasim Zafar⁸, Mohammad Tauheed Ahmad⁹

Affiliations

¹ Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz, Jeddah 21589, Saudi Arabia.
² Department of Health Information Management and Technology, College of Applied Medical Sciences, University of Hafr Al-Batin, Hafr Al-Batin 39524, Saudi Arabia.
³ Department of Electrical Engineering, College of Engineering, Jazan University, P.O. Box 706, Jazan 45142, Saudi Arabia.
⁴ Department of Business Administration, Applied College, Jazan University, P.O. Box 706, Jazan 45142, Saudi Arabia.
⁵ Government Medical College Siddipet, Ensanpalli, Siddipet District, Telangana 502114, India.
⁶ Aerospace Engineering Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
⁷ Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
⁸ Department of Computer Science, Aligarh Muslim University, Aligarh 202002, India.
⁹ College of Medicine, King Khalid University, Abha 62217, Saudi Arabia.

PMID: 37296738
PMCID: PMC10252377
DOI: 10.3390/diagnostics13111886

A Novel COVID-19 Diagnostic System Using Biosensor Incorporated Artificial Intelligence Technique

Md Mottahir Alam et al. Diagnostics (Basel). 2023.

. 2023 May 28;13(11):1886.

doi: 10.3390/diagnostics13111886.

Authors

Md Mottahir Alam¹, Md Moddassir Alam², Hidayath Mirza³, Nishat Sultana⁴, Nazia Sultana⁵, Amjad Ali Pasha⁶, Asif Irshad Khan⁷, Aasim Zafar⁸, Mohammad Tauheed Ahmad⁹

Affiliations

¹ Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz, Jeddah 21589, Saudi Arabia.
² Department of Health Information Management and Technology, College of Applied Medical Sciences, University of Hafr Al-Batin, Hafr Al-Batin 39524, Saudi Arabia.
³ Department of Electrical Engineering, College of Engineering, Jazan University, P.O. Box 706, Jazan 45142, Saudi Arabia.
⁴ Department of Business Administration, Applied College, Jazan University, P.O. Box 706, Jazan 45142, Saudi Arabia.
⁵ Government Medical College Siddipet, Ensanpalli, Siddipet District, Telangana 502114, India.
⁶ Aerospace Engineering Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
⁷ Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
⁸ Department of Computer Science, Aligarh Muslim University, Aligarh 202002, India.
⁹ College of Medicine, King Khalid University, Abha 62217, Saudi Arabia.

PMID: 37296738
PMCID: PMC10252377
DOI: 10.3390/diagnostics13111886

Abstract

COVID-19, continually developing and raising increasingly significant issues, has impacted human health and caused countless deaths. It is an infectious disease with a high incidence and mortality rate. The spread of the disease is also a significant threat to human health, especially in the developing world. This study suggests a method called shuffle shepherd optimization-based generalized deep convolutional fuzzy network (SSO-GDCFN) to diagnose the COVID-19 disease state, types, and recovered categories. The results show that the accuracy of the proposed method is as high as 99.99%; similarly, precision is 99.98%; sensitivity/recall is 100%; specificity is 95%; kappa is 0.965%; AUC is 0.88%; and MSE is less than 0.07% as well as 25 s. Moreover, the performance of the suggested method has been confirmed by comparison of the simulation results from the proposed approach with those from several traditional techniques. The experimental findings demonstrate strong performance and high accuracy for categorizing COVID-19 stages with minimal reclassifications over the conventional methods.

Keywords: COVID-19; artificial intelligence; biosensor; feature extraction; hyperparameter; optimization.

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Conflict of interest statement

The authors declare that there are no competing interests.

Figures

**Figure 1**
Proposed model’s architecture.

**Figure 2**
Proposed HO-GINFC predictive control methodology.

**Figure 3**
Flowchart of SSO algorithm.

**Figure 4**
Comparison of the accuracy of (a) training and (b) testing.

**Figure 5**
Comparison of the loss (a) training and (b) testing.

**Figure 6**
Comparison of performance metrics (a) accuracy, (b) precision, (c) sensitivity, (d) specificity, and (e) kappa.

**Figure 7**
Comparative analysis of ROC curve for COVID-19 variant classification.

**Figure 8**
MSE metrics (a) proposed model output and (b) comparative analysis.

**Figure 9**
Comparative performance of execution time.

See this image and copyright information in PMC

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A Novel COVID-19 Diagnostic System Using Biosensor Incorporated Artificial Intelligence Technique

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A Novel COVID-19 Diagnostic System Using Biosensor Incorporated Artificial Intelligence Technique

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