Performance analysis of data mining algorithms for diagnosing COVID‑19
نویسندگان
1 Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran Student Research Committee Department, Abadan University of Medical Sciences, Abadan, Iran
2 Department of Health Information Technology, School of Paramedical, Ilam University of Medical Sciences, Ilam, Iran
3 Department of English Language, School of Medicine, Ilam University of Medical Science, Ilam, Iran
4 Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
doi
چکیده
BACKGROUND: An outbreak of atypical pneumonia termed COVID‑19 has widely spread all overthe world since the beginning of 2020. In this regard, designing a prediction system for the earlydetection of COVID‑19 is a critical issue in mitigating virus spread. In this study, we have appliedselected 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 recordedfrom the Imam Khomeini Hospital database between May 9, 2020 and December 20, 2020, havebeen taken into consideration. The Chi‑square method was used to determine the most importantfeatures in diagnosing the COVID‑19; eight selected data mining algorithms including multilayerperceptron (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 mostappropriate diagnostic model for COVID‑19 was obtained based on comparing the performance ofthe selected algorithms.RESULTS: As the result of using the Chi‑square method, 21 variables were identified as themost important diagnostic criteria in COVID‑19. The results of evaluating the eight selected datamining 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 areaunder the receiver operator characteristics = 0.68, respectively, had the higher performance thanthe other algorithms.CONCLUSION: The results of evaluating the performance criteria showed that the J‑48 can beconsidered as a suitable computational prediction model for diagnosing COVID‑19 disease.
