. Raoof Nopour; . Mostafa Shanbehzadeh; . Hadi Kazemi‑Arpanahi
Volume 12, Issue 5 , June 2022, , Pages 1-6
Abstract
BACKGROUND: The main manifestations of coronavirus disease‑2019 (COVID‑19) are similarto the many other respiratory diseases. In addition, the existence of numerous uncertainties ...
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BACKGROUND: The main manifestations of coronavirus disease‑2019 (COVID‑19) are similarto the many other respiratory diseases. In addition, the existence of numerous uncertainties inthe prognosis of this condition has multiplied the need to establish a valid and accurate predictionmodel. This study aimed to develop a diagnostic model based on logistic regression to enhance thediagnostic accuracy of COVID‑19.MATERIALS AND METHODS: A standardized diagnostic model was developed on data of 400patients who were referred to Ayatollah Talleghani Hospital, Abadan, Iran, for the COVID‑19diagnosis. We used the Chi‑square correlation coefficient for feature selection, and logistic regressionin SPSS V25 software to model the relationship between each of the clinical features. Potentiallydiagnostic determinants extracted from the patient’s history, physical examination, and laboratoryand imaging testing were entered in a logistic regression analysis. The discriminative ability of themodel was expressed as sensitivity, specificity, accuracy, and area under the curve, respectively.RESULTS: After determining the correlation of each diagnostic regressor with COVID‑19 usingthe Chi‑square method, the 15 important regressors were obtained at the level of P < 0.05. Theexperimental 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 healthcareresources in fighting against this pandemic require increasing attention to using the Clinical DecisionSupport Systems equipped with supervised learning classification algorithms such as logisticregression.
