Abstract
The COVID-19 outbreak is currently one of the biggest challenges facing countries around the world. Millions of people have lost their lives due to COVID-19. Therefore, the accurate early detection and identification of severe COVID-19 cases can reduce the mortality rate and the likelihood of further complications. Machine Learning (ML) and Deep Learning (DL) models have been shown to be effective in the detection and diagnosis of several diseases, including COVID-19. This study used ML algorithms, such as Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and K-Nearest Neighbor (KNN) and DL model (containing six layers with ReLU and output layer with sigmoid activation), to predict the mortality rate in COVID-19 cases. Models were trained using confirmed COVID-19 patients from 146 countries. Comparative analysis was performed among ML and DL models using a reduced feature set. The best results were achieved using the proposed DL model, with an accuracy of 0.97. Experimental results reveal the significance of the proposed model over the baseline study in the literature with the reduced feature set.
Highlights
Results showed using the Medical Information Mart for Intensive Care (MIMIC) dataset for training and testing the XGBoost obtained for mortality prediction of ventilated ICU patients with an Area Under Curve (AUC) of 0.82, 0.81, 0.77, 0.75 for hour windows of, 12, 24, 48, and 72 h, respectively
The community hospital dataset for testing the mortality prediction the AUC values were 0.91, 0.90, 0.86, 0.87 for hour windows of
The results show that RBFNN and Probabilistic Neural Network (PNN) had the best performance in both datasets
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. COVID-19 is a highly contagious disease caused by a novel coronavirus. It is known that elderly people and those with chronic diseases are more likely to experience severe illness [1]. This has put enormous strain and pressure on governments and healthcare organizations, as well as on their limited resources. Res. Public Health 2021, 18, 6429 populations of heterogeneous COVID-19 cases. Public Health 2021, 18, 6429 populations of heterogeneous COVID-19 cases These methods can help to automatically extract and identify the relationship among complex attributes. The study compared the ML and DL models developed to predict the rate of mortality in COVID-19 cases using sociodemographic and clinical data. In the last section, the conclusion and recommendations for future research are discussed
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