Performance analysis of data mining algorithms for diagnosing COVID-19.
J Educ Health Promot
; 10: 405, 2021.
Article
in English
| MEDLINE | ID: covidwho-1596494
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 ANDMETHODS:
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Type of study:
Experimental Studies
/
Prognostic study
/
Randomized controlled trials
/
Reviews
Language:
English
Journal:
J Educ Health Promot
Year:
2021
Document Type:
Article
Affiliation country:
Jehp.jehp_138_21
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