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Machine learning approach to predicting COVID-19 disease severity based on clinical blood test data: statistical analysis and model development

Aktar, Safika, Ahamad, Md Martuza, Rashed-Al-Mahfuz, Md, Azad, AKM, Uddin, Shahadat, Kamal, AHM, Alyami, Salem A, Lin, Ping-I, Shariful Islam, Sheikh Mohammed, Quinn, Julian MW, Eapen, Valsamma and Moni, Mohammad Ali 2021, Machine learning approach to predicting COVID-19 disease severity based on clinical blood test data: statistical analysis and model development, JMIR medical informatics, vol. 9, no. 4, pp. 1-15, doi: 10.2196/25884.

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Title Machine learning approach to predicting COVID-19 disease severity based on clinical blood test data: statistical analysis and model development
Author(s) Aktar, Safika
Ahamad, Md Martuza
Rashed-Al-Mahfuz, Md
Azad, AKM
Uddin, Shahadat
Kamal, AHM
Alyami, Salem A
Lin, Ping-I
Shariful Islam, Sheikh MohammedORCID iD for Shariful Islam, Sheikh Mohammed orcid.org/0000-0001-7926-9368
Quinn, Julian MW
Eapen, Valsamma
Moni, Mohammad Ali
Journal name JMIR medical informatics
Volume number 9
Issue number 4
Article ID e25884
Start page 1
End page 15
Total pages 15
Publisher JMIR Publications
Place of publication Toronto, Ont.
Publication date 2021
ISSN 2291-9694
Keyword(s) COVID-19
blood samples
machine learning
statistical analysis
prediction
severity
mortality
morbidity
risk
blood
testing
outcome
data set
Summary Accurate prediction of the disease severity of patients with COVID-19 would greatly improve care delivery and resource allocation and thereby reduce mortality risks, especially in less developed countries. Many patient-related factors, such as pre-existing comorbidities, affect disease severity and can be used to aid this prediction. OBJECTIVE Because rapid automated profiling of peripheral blood samples is widely available, we aimed to investigate how data from the peripheral blood of patients with COVID-19 can be used to predict clinical outcomes. METHODS We investigated clinical data sets of patients with COVID-19 with known outcomes by combining statistical comparison and correlation methods with machine learning algorithms; the latter included decision tree, random forest, variants of gradient boosting machine, support vector machine, k-nearest neighbor, and deep learning methods. RESULTS Our work revealed that several clinical parameters that are measurable in blood samples are factors that can discriminate between healthy people and COVID-19–positive patients, and we showed the value of these parameters in predicting later severity of COVID-19 symptoms. We developed a number of analytical methods that showed accuracy and precision scores >90% for disease severity prediction. CONCLUSIONS We developed methodologies to analyze routine patient clinical data that enable more accurate prediction of COVID-19 patient outcomes. With this approach, data from standard hospital laboratory analyses of patient blood could be used to identify patients with COVID-19 who are at high risk of mortality, thus enabling optimization of hospital facilities for COVID-19 treatment.
Language eng
DOI 10.2196/25884
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30150076

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.