. Mostafa Shanbehzadeh; . Hadi Kazemi-Arpanahi; . Azam Orooji; . Sara Mobarak; . Saeed Jelvay
Volume 11, Issue 7 , August 2021, , Pages 1-11
Abstract
BACKGROUND: Given coronavirus disease (COVID‑19’s) unknown nature, diagnosis, and treatmentis very complex up to the present time. Thus, it is essential to have a framework ...
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BACKGROUND: Given coronavirus disease (COVID‑19’s) unknown nature, diagnosis, and treatmentis very complex up to the present time. Thus, it is essential to have a framework for an early predictionof the disease. In this regard, machines learning (ML) could be crucial to extract concealed patternsfrom 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 bestperformance of them.MATERIALS AND METHODS: The dataset of Ayatollah Talleghani hospital, COVID‑19 focal centeraffiliated to Abadan University of Medical Sciences have been taken into consideration. The datasetused in this study consists of 501 case records with two classes (COVID‑19 and non COVID‑19) and32 columns for the diagnostic features. ML algorithms such as Naïve Bayesian, Bayesian Net, randomforest (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 suchas 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%, specificityof 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 comparedto other six classification models. It is promising to the implementation of RF model in the health‑caresettings to increase the accuracy and speed of disease diagnosis for primary prevention, screening,surveillance, and early treatment.