Document Type : Original Article


1 Assistant Professor of Health Information Management, Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran

2 Assistant Professor of Health Information Management, Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran, Assistant Professor of Health Information Management, Student Research Committee, Abadan University of Medical Sciences, Abadan, Iran

3 Assistant Professor of Medical Informatics, School of Medicine, North Khorasan University of Medical Science, North Khorasan, Iran

4 Assistant Professor of Infectious Diseases, School of Medicine, Abadan University of Medical Sciences, Abadan, Iran

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


BACKGROUND: Given coronavirus disease (COVID‑19’s) unknown nature, diagnosis, and treatment
is very complex up to the present time. Thus, it is essential to have a framework for an early prediction
of the disease. In this regard, machines learning (ML) could be crucial to extract concealed patterns
from mining of huge raw datasets then it establishes high‑quality predictive models. At this juncture,
we aimed to apply different ML techniques to develop clinical predictive models and select the best
performance of them.
MATERIALS AND METHODS: The dataset of Ayatollah Talleghani hospital, COVID‑19 focal center
affiliated to Abadan University of Medical Sciences have been taken into consideration. The dataset
used in this study consists of 501 case records with two classes (COVID‑19 and non COVID‑19) and
32 columns for the diagnostic features. ML algorithms such as Naïve Bayesian, Bayesian Net, random
forest (RF), multilayer perceptron, K‑star, C4.5, and support vector machine were developed. Then,
the recital of selected ML models was assessed by the comparison of some performance indices such
as accuracy, sensitivity, specificity, precision, F‑score, and receiver operating characteristic (ROC).
RESULTS: The experimental results indicate that RF algorithm with the accuracy of 92.42%, specificity
of 75.70%, precision of 92.30%, sensitivity of 92.40%, F‑measure of 92.00%, and ROC of 97.15%
has the best capability for COVID‑19 diagnosis and screening.
CONCLUSION: The empirical results reveal that RF model yielded higher performance as compared
to other six classification models. It is promising to the implementation of RF model in the health‑care
settings to increase the accuracy and speed of disease diagnosis for primary prevention, screening,
surveillance, and early treatment.


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