Decision making model for detecting infected people with COVID-19

نویسندگان

چکیده

The detection of people that are infected with COVID-19 is critical issue due to the high variance appearing symptoms between them. Therefore, different medical tests adopted detect patients, such as Polymerase Chain Reaction (PCR) and SARS-CoV-2 Antibodies. In order produce a model for detecting people, decision-making techniques can be utilized. this paper, decision tree technique based Decisive Decision Tree (DDT) considered propose an optimized approach negative PCR test results using antibodies Complete Blood Count (CBC) test. Moreover, fever cough have been well improve design tree, in which precision increased well. proposed DDT provide three classes Infected (I), Not (NI), Suspected (S) on parameters. tested over patients? samples off real-time simulation, obtained show satisfactory class accuracy ratio varies from 95% 100%.

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ژورنال

عنوان ژورنال: Yugoslav Journal of Operations Research

سال: 2023

ISSN: ['2334-6043', '0354-0243', '1820-743X']

DOI: https://doi.org/10.2298/yjor221115009m