Repository of Research and Investigative Information

Repository of Research and Investigative Information

Ilam University of Medical Sciences

Machine Learning-Based Clinical Decision Support System for Automatic Diagnosis of COVID-19 based on Clinical Data

Thu Jul 25 22:54:10 2024

(2022) Machine Learning-Based Clinical Decision Support System for Automatic Diagnosis of COVID-19 based on Clinical Data. Journal of Biostatistics and Epidemiology. pp. 77-89. ISSN 23834196 (ISSN)

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

Abstract

Introduction: Needless to say that correct and real-time detection and effective prognosis of the COVID-19 are necessary to deliver the best possible care for patients and, accordingly, diminish the pressure on the healthcare industries. Hence our paper aims to present an intelligent algorithm for selecting the best features from the dataset and developing Machine Learning(ML) based models to predict the COVID-19 and finally opted for the best-performing algorithm. Methods: In this developmental study, the clinical data of 1703 COVID-19 and non-COVID-19 patients Using a single-center registry from February 9, 2020, to December 20, 2020, were used. The Minimum Redundancy Maximum Relevance (mRMR) feature selection algorithm identified the most relevant variables. Then, chosen features feed into the several data mining methods, including K-Nearest Neighbors, AdaBoost Classifier, Decision Tree, HistGradient Boosting Classifier, and Support Vector Machine. A 10-fold cross-validation method and six performance evaluation metrics were used to evaluate and compare these implemented algorithms, and finally, the best model was implemented. Results: Out of the 34 included features, 11 variables were selected as the essential features. The results of using ML algorithms indicated that the best performance belongs to the AdaBoost classifier with mean accuracy = 92.9, mean specificity = 89.3, mean sensitivity = 94.2, mean F-measure = 91.6 , mean KAPA = 94.3 and mean ROC = 92.1 . Conclusion: The empirical results reveal that the Adaboost model yielded higher performance than other classification models and developed our Clinical Decision Support Systems (CDSS) interface to discriminate positive COVID-19 from negative cases. © 2022 Tehran University of Medical Sciences. Published by Tehran University of Medical Sciences. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International license (https://creativecommons.org/licenses/by-nc/4.0/). Noncommercial uses of the work are permitted, provided the original work is properly cited.

Item Type: Article
Creators:
CreatorsEmail
Afrash, M. R.UNSPECIFIED
Erfannia, L.UNSPECIFIED
Amraei, M.UNSPECIFIED
Mehrabi, N.UNSPECIFIED
Jelvay, S.UNSPECIFIED
Nopour, R.UNSPECIFIED
Shanbehzadeh, M.UNSPECIFIED
Keywords: Artificial intelligence Coronavirus COVID-19 Decision Support Systems Machine learning
Divisions:
Page Range: pp. 77-89
Journal or Publication Title: Journal of Biostatistics and Epidemiology
Journal Index: Scopus
Volume: 8
Number: 1
ISSN: 23834196 (ISSN)
Depositing User: مهندس مهدی شریفی
URI: http://eprints.medilam.ac.ir/id/eprint/4148

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