Computational Intelligence-Based Model for Mortality Rate Prediction in COVID-19 Patients

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.

Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic

“…First, we compare studies that are concerned with using AI in COVID-19 diagnosis through medical images. Based on this comparison, we observed that (i) a large number of studies have utilized CT scans and X-rays in their works [ 243 , 270 , 271 ], where few studies utilized lung US [ 55 , 66 , 272 ]; (ii) although X-ray chest scans are considered less sensitive than PCR tests in detection of COVID-19 at the early stages, it is recommended for monitoring and evaluating the progression of a patient’s status, especially with critical cases [ 215 ]; (iii) segmentation techniques that used to detect the infected region are primarily used in CT scans [ 273 ]; (iv) augmentation techniques that used to increase the size of the dataset are commonly used with X-ray datasets [ 274 ]; (v) the majority of COVID-19 studies utilized CNN in their classification process [ 52 , 275 ], where some of them integrate CNN and transfer learning to overcome the shortage of the available dataset and increase the accuracy of the model [ 32 , 201 , 276 ]; (vi) a small number of studies augmented CNN with random forest and support vector machines to make feature extraction and classification [ 277 , 278 ]; (vii) higher accuracy reported from studies that augmented CNN, transfer learning, and SVM, where using CNN and DL are reported to overfit in some studies due to the shortage of available datasets [ 37 , 162 ]; (viii) accuracy of diagnosis using X-rays in diagnosis is approximately equal to the accuracy when using CT chest scans; (ix) the sensitivity of X-ray in diagnosis is highly correlated with the difference between the time of the initial symptoms and the procedural images;—it was not more than 55% after 2 days from the initial symptoms and increased to 79% after 11 days from the symptom onset [ 147 ]; (x) VGG, MobileNet, and ResNet are the most commonly pre-trained models employed for the classification tasks [ 21 , 52 ]; (xi) explainability of CNN model have been rarely used in clarifying the results of CNN [ 57 ]; and (xii) most of the studies reported accuracies of more than 90% for the binary classification tasks (i.e., COVID-19, non-COVID-19) [ 218 , 279 ], and reported accuracies higher than 80% for three classification tasks (i.e., normal, viral pneumonia, and COVID-19) [ 216 , …”

“…For example, in [ 146 ], the authors surveyed organ complications study and showed that about 3.75% of COVID-19 patients reported abnormalities in liver enzymes, 10% developed acute kidney injury, and 23% were afflicted with heart problems. Researchers in [ 147 ] developed a DL model to analyze the relationship between mortality and other medical comorbidities. They concluded that medical comorbidities are highly associated with mortality, with percentages of 2.56%, 10.3%, 41.0%, and 6% for heart rate problems, respiratory disease, hypertension, and diabetes; the same trend was found in [ 148 , 149 , 150 , 151 , 152 ].…”

Ability of a Machine Learning Algorithm to Predict the Need for Perioperative Red Blood Cells Transfusion in Pelvic Fracture Patients: A Multicenter Cohort Study in China

“…XGBoost has been widely accepted as the one of the models with the most impressive predictive accuracy (29). Moreover, because XGBoost used parallelism, it has been known for its ability to learn quickly and scale appropriately to the problem (30). XGBoost could provide both performance and speed, which was significant and necessary for perioperative blood transfusion.…”

Explainable Artificial Intelligence Approach for the Early Prediction of Ventilator Support and Mortality in COVID-19 Patients

“…They achieved an accuracy of 0.89 using ANN. In addition, Khan et al [22] made a comparison between the ML and DL algorithms to predict mortality in COVID-19 patients using the dataset proposed in Pourhomayoun and Shakibi [21]. They found the significance of the DL method in the early prediction of mortality as compared to the ML algorithms.…”