Document Type : Original Article
Authors
1 Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
2 Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran Student Research Committee Department, Abadan University of Medical Sciences, Abadan, Iran
3 Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
4 Department of English Language, School of Medicine, Ilam University of Medical Science, Ilam, Iran
Abstract
BACKGROUND: An outbreak of atypical pneumonia termed COVID‑19 has widely spread all over
the world since the beginning of 2020. In this regard, designing a prediction system for the early
detection of COVID‑19 is a critical issue in mitigating virus spread. In this study, we have applied
selected machine learning techniques to select the best predictive models based on their performance.
MATERIALS AND METHODS: The data of 435 suspicious cases with COVID‑19 which were recorded
from the Imam Khomeini Hospital database between May 9, 2020 and December 20, 2020, have
been taken into consideration. The Chi‑square method was used to determine the most important
features in diagnosing the COVID‑19; eight selected data mining algorithms including multilayer
perceptron (MLP), J‑48, Bayesian Net (Bayes Net), logistic regression, K‑star, random forest,
Ada‑boost, and sequential minimal optimization (SMO) were applied in data mining. Finally, the most
appropriate diagnostic model for COVID‑19 was obtained based on comparing the performance of
the selected algorithms.
RESULTS: As the result of using the Chi‑square method, 21 variables were identified as the
most important diagnostic criteria in COVID‑19. The results of evaluating the eight selected data
mining algorithms showed that the J‑48 with true‑positive rate = 0.85, false‑positive rate = 0.173,
precision = 0.85, recall = 0.85, F‑score = 0.85, Matthews Correlation Coefficient = 0.68, and area
under the receiver operator characteristics = 0.68, respectively, had the higher performance than
the other algorithms.
CONCLUSION: The results of evaluating the performance criteria showed that the J‑48 can be
considered as a suitable computational prediction model for diagnosing COVID‑19 disease.
Keywords
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