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

Abstract

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.

Comparative Study Based on Analysis of Coronavirus Disease (COVID-19) Detection and Prediction Using Machine Learning Models

“…The proposed pipeline achieves a classification accuracy of 99.65% using support vector classifier [ 30 ] 2020 Transfer learning Convolutional neural networks VGG16, ResNet50, Inception v3 In this study, the transfer learning technique has been applied to clinical images. Texture feature extraction is accomplished using Haralick features which focus only to detect COVID-19 using statistical analyses [ 31 ] 2021 Supervised learning Classification Random Forest, Logistic Regression, Extreme Gradient Boosting Out of all the three methods, Random Forest gave more accuracy of 0.952. But, due to insufficient dataset, data imbalance occurs in this proposed approach.…”

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“…Before analyzing, they partitioned data using 10 k-cross validation and fixed data imbalance using synthetic minority over-sampling technique (SMOTE). They concluded that RF performed better than the other two algorithms [22].…”

An Efficient Covid19 Epidemic Analysis and Prediction Model Using Machine Learning Algorithms

“…Prathyusha K. [4] et.al applied various machine learning regression algorithms like linear regressor, polynomial regressor and achieved the best results with the polynomial regression technique. A. Lakshmanarao [5] et.al applied various regression techniques for analyzing and predicting corona disease and achieved good results with linear regression. Sumayh S. Aljameel [6] et.al applied three classification algorithms random forest, Gradient Boosting, and Logistic Regression.…”