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

被引:3
|
作者
Alam, Md. Mottahir [1 ]
Alam, Md. Moddassir [2 ]
Mirza, Hidayath [3 ]
Sultana, Nishat [4 ]
Sultana, Nazia [5 ]
Pasha, Amjad Ali [6 ]
Khan, Asif Irshad [7 ]
Zafar, Aasim [8 ]
Ahmad, Mohammad Tauheed [9 ]
机构
[1] King Abdulaziz, Fac Engn, Dept Elect & Comp Engn, Jeddah 21589, Saudi Arabia
[2] Univ Hafr Al Batin, Coll Appl Med Sci, Dept Hlth Informat Management & Technol, Hafar al Batin 39524, Saudi Arabia
[3] Jazan Univ, Coll Engn, Dept Elect Engn, POB 706, Jazan 45142, Saudi Arabia
[4] Jazan Univ, Appl Coll, Dept Business Adm, POB 706, Jazan 45142, Saudi Arabia
[5] Govt Med Coll Siddipet, Ensanpalli 502114, Telangana, India
[6] King Abdulaziz Univ, Aerosp Engn Dept, Jeddah 21589, Saudi Arabia
[7] King Abdulaziz Univ, Fac Comp & Informat Technol, Comp Sci Dept, Jeddah 21589, Saudi Arabia
[8] Aligarh Muslim Univ, Dept Comp Sci, Aligarh 202002, India
[9] King Khalid Univ, Coll Med, Abha 62217, Saudi Arabia
关键词
biosensor; artificial intelligence; COVID-19; optimization; feature extraction; hyperparameter;
D O I
10.3390/diagnostics13111886
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
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.
引用
收藏
页数:23
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