Journal article
Machine Learning-Based Model to Predict the Disease Severity and Outcome in COVID-19 Patients


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Author list: Sumayh S. Aljameel, Irfan Ullah Khan, Nida Aslam, Malak Aljabri, and Eman S. Alsulmi
Publisher: Hindawi
Publication year: 2021
Journal: Scientific Programming
Volume number: 2021
Start page: 1
End page: 10
Number of pages: 10
ISSN: 1058-9244
Web of Science ID:
PubMed ID:
Scopus ID: 85105331575
eISSN: 1875-919X
Languages: English-United States


The novel coronavirus (COVID-19) outbreak produced devastating effects on the global economy and the health of entire communities. Although the COVID-19 survival rate is high, the number of severe cases that result in death is increasing daily. A timely prediction of at-risk patients of COVID-19 with precautionary measures is expected to increase the survival rate of patients and reduce the fatality rate. This research provides a prediction method for the early identification of COVID-19 patient’s outcome based on patients’ characteristics monitored at home, while in quarantine. The study was performed using 287 COVID-19 samples of patients from the King Fahad University Hospital, Saudi Arabia. The data were analyzed using three classification algorithms, namely, logistic regression (LR), random forest (RF), and extreme gradient boosting (XGB). Initially, the data were preprocessed using several preprocessing techniques. Furthermore, 10-k cross-validation was applied for data partitioning and SMOTE for alleviating the data imbalance. Experiments were performed using twenty clinical features, identified as significant for predicting the survival versus the deceased COVID-19 patients. The results showed that RF outperformed the other classifiers with an accuracy of 0.95 and area under curve (AUC) of 0.99. The proposed model can assist the decision-making and health care professional by early identification of at-risk COVID-19 patients effectively.


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Last updated on 2021-20-05 at 11:31