Machine Learning-Based Clinical Decision Support System for automatic diagnosis of COVID-19 based on the routine blood test
Background: Needless to say that correct and real-time detection and effective prognosis of the COVID-19 are necessary measures to deliver the best possible care for patients and, accordingly, diminish the pressure on the health care industries. The main purpose of the present paper was to devise practical solutions based on Machine Learning (ML) techniques to ease the COVID-19 screening in routine blood test data. We came up with different algorithms for the early detection of COVID-19 and finally succeeded to opt for the best performing algorithm. Material and methods: In this developmental study, the laboratory data of 1703 COVID-19 and non-COVID-19 patients Using a single-center registry from February 9, 2020, to December 20, 2020, were analyzed by several ML algorithms which included, K-Nearest Neighbors, AdaBoost Classifier, Decision Tree, and HistGradient Boosting Classifier. A 10-fold cross-validation method and six performance evaluation metrics were used to evaluate and compare these implemented algorithms. Using the best ML-developed model, a Clinical Decision Support System (CDSS) was implemented with C# programming language. Results: The results indicated that the best performance belongs to the AdaBoost classifier with mean accuracy, specificity, sensitivity, F-measure, KAPA rate, and ROC of 87.1%, 85.3%, 87.3%, 87.1 %, 89.4%, and 87.3 % respectively Discussion: The ML makes a reasonable level of accuracy possible for an early diagnosis and screening of COVID-19. The empirical results reveal that the Adaboost model yielded higher performance compared with other classification models and was used for developing our CDSS interface in discriminates positive COVID-19 from negative cases..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:8 |
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| Enthalten in: |
Journal of Biostatistics and Epidemiology - 8(2022), 1 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Mohammad Reza Afrash [VerfasserIn] |
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| Links: |
doaj.org [kostenfrei] |
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| Themen: |
: COVID-19 |
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| Förderinstitution / Projekttitel: |
|
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| PPN (Katalog-ID): |
DOAJ023999845 |
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| 520 | |a Background: Needless to say that correct and real-time detection and effective prognosis of the COVID-19 are necessary measures to deliver the best possible care for patients and, accordingly, diminish the pressure on the health care industries. The main purpose of the present paper was to devise practical solutions based on Machine Learning (ML) techniques to ease the COVID-19 screening in routine blood test data. We came up with different algorithms for the early detection of COVID-19 and finally succeeded to opt for the best performing algorithm. Material and methods: In this developmental study, the laboratory data of 1703 COVID-19 and non-COVID-19 patients Using a single-center registry from February 9, 2020, to December 20, 2020, were analyzed by several ML algorithms which included, K-Nearest Neighbors, AdaBoost Classifier, Decision Tree, and HistGradient Boosting Classifier. A 10-fold cross-validation method and six performance evaluation metrics were used to evaluate and compare these implemented algorithms. Using the best ML-developed model, a Clinical Decision Support System (CDSS) was implemented with C# programming language. Results: The results indicated that the best performance belongs to the AdaBoost classifier with mean accuracy, specificity, sensitivity, F-measure, KAPA rate, and ROC of 87.1%, 85.3%, 87.3%, 87.1 %, 89.4%, and 87.3 % respectively Discussion: The ML makes a reasonable level of accuracy possible for an early diagnosis and screening of COVID-19. The empirical results reveal that the Adaboost model yielded higher performance compared with other classification models and was used for developing our CDSS interface in discriminates positive COVID-19 from negative cases. | ||
| 650 | 4 | |a : COVID-19 | |
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| 653 | 0 | |a Biology (General) | |
| 653 | 0 | |a Probabilities. Mathematical statistics | |
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