Repository of Research and Investigative Information

Repository of Research and Investigative Information

Ilam University of Medical Sciences

Using logistic regression to develop a diagnostic model for COVID-19: A single-center study

Fri Apr 19 00:15:50 2024

(2022) Using logistic regression to develop a diagnostic model for COVID-19: A single-center study. Journal of education and health promotion. p. 153. ISSN 2277-9531 (Print) 2277-9531 (Linking)

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BACKGROUND: The main manifestations of coronavirus disease-2019 (COVID-19) are similar to the many other respiratory diseases. In addition, the existence of numerous uncertainties in the prognosis of this condition has multiplied the need to establish a valid and accurate prediction model. This study aimed to develop a diagnostic model based on logistic regression to enhance the diagnostic accuracy of COVID-19. MATERIALS AND METHODS: A standardized diagnostic model was developed on data of 400 patients who were referred to Ayatollah Talleghani Hospital, Abadan, Iran, for the COVID-19 diagnosis. We used the Chi-square correlation coefficient for feature selection, and logistic regression in SPSS V25 software to model the relationship between each of the clinical features. Potentially diagnostic determinants extracted from the patient's history, physical examination, and laboratory and imaging testing were entered in a logistic regression analysis. The discriminative ability of the model was expressed as sensitivity, specificity, accuracy, and area under the curve, respectively. RESULTS: After determining the correlation of each diagnostic regressor with COVID-19 using the Chi-square method, the 15 important regressors were obtained at the level of P < 0.05. The experimental results demonstrated that the binary logistic regression model yielded specificity, sensitivity, and accuracy of 97.3, 98.8, and 98.2, respectively. CONCLUSION: The destructive effects of the COVID-19 outbreak and the shortage of healthcare resources in fighting against this pandemic require increasing attention to using the Clinical Decision Support Systems equipped with supervised learning classification algorithms such as logistic regression.

Item Type: Article
Shanbehzadeh, M.UNSPECIFIED
Kazemi-Arpanahi, H.UNSPECIFIED
Keywords: Coronavirus coronavirus disease-2019 logistic regression prognostic modeling
Page Range: p. 153
Journal or Publication Title: Journal of education and health promotion
Journal Index: Pubmed
Volume: 11
Identification Number:₁₀₁₇₂₁
ISSN: 2277-9531 (Print) 2277-9531 (Linking)
Depositing User: مهندس مهدی شریفی

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