Repository of Research and Investigative Information

Repository of Research and Investigative Information

Ilam University of Medical Sciences

Performance analysis of data mining algorithms for diagnosing COVID-19

Tue Aug 16 11:06:20 2022

(2021) Performance analysis of data mining algorithms for diagnosing COVID-19. Journal of Education and Health Promotion. ISSN 22779531 (ISSN)

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

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. © 2021 International Union of Crystallography. All rights reserved.

Item Type: Article
Creators:
CreatorsEmail
Nopour, R.UNSPECIFIED
Kazemi-Arpanahi, H.UNSPECIFIED
Shanbehzadeh, M.UNSPECIFIED
Azizifar, A.UNSPECIFIED
Keywords: Artificial intelligence coronavirus COVID-19 data mining diagnosis machine learning
Divisions:
Journal or Publication Title: Journal of Education and Health Promotion
Journal Index: Scopus
Volume: 10
Number: 1
Identification Number: https://doi.org/10.4103/jehp.jehp₁₃₈₂₁
ISSN: 22779531 (ISSN)
Depositing User: مهندس مهدی شریفی
URI: http://eprints.medilam.ac.ir/id/eprint/3777

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