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- Analysis of Symptoms and Demographic Characteristics in Diagnosis of COVID-19 by Logistic Regression...
Analysis of Symptoms and Demographic Characteristics in Diagnosis of COVID-19 by Logistic Regression Model
Authors : Caner Tanış
Pages : 1-5
Doi:10.35238/sufefd.1335965
View : 46 | Download : 36
Publication Date : 2024-04-24
Article Type : Research
Abstract :The new coronavirus COVID-19 is an infectious disease that started spreading globally in December 2019. Some symptoms are known to give clues as to whether the COVID-19 virus is infected. Therefore, the main purpose of this paper was to determine specific symptoms related to COVID-19 for the rapid diagnosis of COVID-19 cases. The data set consists of 25985 individuals including PCR results, 2 demographic properties (age, gender), and 5 symptoms such as headache, shortness of breath, sore throat, fever, and cough is considered in this study. We analyzed the relationship between these covariates and PCR results by binary logistic regression model. A total of 16405 (63.1%) individuals having to positive PCR results were included in this study. The research population was divided into two age groups (<60 and ≥60). The findings regarding the symptoms observed in COVID-19 patients can be listed as follows: Headache (25.8%), shortness of breath (2.2%), sore throat (11.2%), fever (16.3%), and cough (26.2%). The findings of binary logistic regression analysis show that any individual in the elder group has more probability of a positive PCR result approximately 1.6 times (odds ratio [OR]: 1.681), 95% confidence interval [CI]: 1.535-1.840). Also, an individual with symptoms of headache is approximately %7 more likely to have a positive PCR result than a nonexistent one (OR: 1.068, CI: 1.006-1.135).Keywords : İkili lojistik regresyon, COVID-19, Bulaşıcı hastalık, Semptomlar, Odds oranı