Repository of Research and Investigative Information

Repository of Research and Investigative Information

Ilam University of Medical Sciences

Performance evaluation of selected machine learning algorithms for COVID-19 prediction using routine clinical data: With versus Without CT scan features

Thu Aug 18 12:37:08 2022

(2021) Performance evaluation of selected machine learning algorithms for COVID-19 prediction using routine clinical data: With versus Without CT scan features. Journal of Education and Health Promotion. p. 285. ISSN 2277-9531 (Print) 2277-9531 (Linking)

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Official URL: https://www.ncbi.nlm.nih.gov/pubmed/34667785

Abstract

BACKGROUND: Given coronavirus disease (COVID-19's) unknown nature, diagnosis, and treatment is very complex up to the present time. Thus, it is essential to have a framework for an early prediction of the disease. In this regard, machines learning (ML) could be crucial to extract concealed patterns from 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 best performance of them. MATERIALS AND METHODS: The dataset of Ayatollah Talleghani hospital, COVID-19 focal center affiliated to Abadan University of Medical Sciences have been taken into consideration. The dataset used in this study consists of 501 case records with two classes (COVID-19 and non COVID-19) and 32 columns for the diagnostic features. ML algorithms such as Naive Bayesian, Bayesian Net, random forest (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 such as 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, specificity of 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 compared to other six classification models. It is promising to the implementation of RF model in the health-care settings to increase the accuracy and speed of disease diagnosis for primary prevention, screening, surveillance, and early treatment.

Item Type: Article
Creators:
CreatorsEmail
Shanbehzadeh, M.UNSPECIFIED
Kazemi-Arpanahi, H.UNSPECIFIED
Orooji, A.UNSPECIFIED
Mobarak, S.UNSPECIFIED
Jelvay, S.UNSPECIFIED
Keywords: Artificial intelligence Covid-19 computed tomography scan coronavirus data mining machine learning random forest
Divisions:
Page Range: p. 285
Journal or Publication Title: Journal of Education and Health Promotion
Journal Index: Pubmed
Volume: 10
Identification Number: https://doi.org/10.4103/jehp.jehp₁₄₂₄₂₀
ISSN: 2277-9531 (Print) 2277-9531 (Linking)
Depositing User: مهندس مهدی شریفی
URI: http://eprints.medilam.ac.ir/id/eprint/3767

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