A type-2 fuzzy rule-based model for diagnosis of COVID-19

被引：1
|
作者：
Sahin, Ihsan [1 ]
Akdogan, Erhan [1 ,2 ]
Aktan, Mehmet Emin [2 ,3 ]
机构：
[1] Yildiz Tech Univ, Fac Mech Engn, Dept Mechatron Engn, Istanbul, Turkiye
[2] Hlth Inst Turkiye, Istanbul, Turkiye
[3] Bartin Univ, Fac Engn Architecture & Design, Dept Mechatron Engn, Bartin, Turkiye
来源：
TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES | 2023年 / 31卷 / 01期
关键词：
COVID-19; fuzzy logic; decision support system; diagnosis; INTERVAL TYPE-2; FRAMEWORK; SEVERITY; SYSTEMS; DRIVEN;
D O I：
10.55730/1300-0632.3970
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this study, a type-2 fuzzy logic-based decision support system comprising clinical examination and blood test results that health professionals can use in addition to existing methods in the diagnosis of COVID-19 has been developed. The developed system consists of three fuzzy units. The first fuzzy unit produces COVID-19 positivity as a percentage according to the respiratory rate, loss of smell, and body temperature values, and the second fuzzy unit according to the C-reactive protein, lymphocyte, and D-dimer values obtained as a result of the blood tests. In the third fuzzy unit, the COVID-19 positivity risks according to the clinical examination and blood analysis results, which are the outputs of the first and second fuzzy units, are evaluated together and the result is obtained. As a result of the evaluation of the trials with 60 different scenarios by physicians, it has been revealed that the system can detect COVID-19 risk with 86.6% accuracy.
引用
收藏
页码：39 / 52
页数：14
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