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

İhsan Şahin, E. Akdogan, Mehmet Emin Aktan
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引用次数: 1

Abstract

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. [ FROM AUTHOR]
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基于2型模糊规则的新型冠状病毒诊断模型
本研究开发了一种基于2型模糊逻辑的决策支持系统,包括临床检查和血液检测结果,供卫生专业人员在现有方法的基础上进行COVID-19诊断。该系统由三个模糊单元组成。第一个模糊单元根据呼吸频率、嗅觉丧失和体温值产生COVID-19阳性的百分比,第二个模糊单元根据血液测试获得的c反应蛋白、淋巴细胞和d -二聚体值产生百分比。在第三个模糊单元中,根据临床检查和血液分析结果,即第一和第二模糊单元的输出,共同评估COVID-19阳性风险,并得出结果。医生对60种不同场景的试验进行了评估,结果显示,该系统对新冠肺炎风险的检测准确率为86.6%。[源自作者]
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