Document Type : Original Article


1 Department of Health Information Management, Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran

2 Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran

3 Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran


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


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