Applying Different Machine Learning Techniques for Prediction of COVID-19 Severity

Abstract

Due to the increase in the number of patients who died as a result of the SARS-CoV-2 virus around the world, researchers are working tirelessly to find technological solutions to help doctors in their daily work. Fast and accurate Artificial Intelligence (AI) techniques are needed to assist doctors in their decisions to predict the severity and mortality risk of a patient. Early prediction of patient severity would help in saving hospital resources and decrease the continual death of patients by providing early medication actions. Currently, X-ray images are used as early symptoms in detecting COVID-19 patients. Therefore, in this research, a prediction model has been built to predict different levels of severity risks for the COVID-19 patient based on X-ray images by applying machine learning techniques. To build the proposed model, CheXNet deep pretrained model and hybrid handcrafted techniques were applied to extract features, two different methods: Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE) were integrated to select the most important features, and then, six machine learning techniques were applied. For handcrafted features, the experiments proved that merging the features that have been selected by PCA and RFE together (PCA+RFE) achieved the best results with all classifiers compared with using all features or using the features selected by PCA or RFE individually. The XGBoost classifier achieved the best performance with the merged (PCA + RFE) features, where it accomplished 97% accuracy, 98% precision, 95% recall, 96% f1-score and 100% roc-auc. Also, SVM carried out the same results with some minor differences, but overall it was a good performance where it accomplished 97% accuracy, 96% precision, 95% recall, 95% f1-score and 99% roc-auc. On the other hand, for pre-trained CheXNet features, Extra Tree and SVM classifiers with RFE achieved 99.6% for all measures.

IoT-Enabled Framework for Early Detection and Prediction of COVID-19 Suspects by Leveraging Machine Learning in Cloud

“…The future predictions can be made using AI and ML in IoT-based systems for predicting the upcoming infection of coronavirus [ 19 ]. The IoT can be used as a data source, and ML is used for data analytics to better further analyze the COVID-19 [ 20 ] to get better insights. With the help of IoT, a centralized information system can be created where all activities are stored electronically and can be accessed anywhere and anytime [ 21 ].…”

A novel hybrid supervised and unsupervised hierarchical ensemble for COVID-19 cases and mortality prediction

“…Autoregressive Integrated Moving Average (ARIMA)and LSTM models demonstrated the highest MAPE errors. Another approach is characterized by the work of S. A.-F. Sayed et al 17 , who built a model predicting various levels of severity risk for COVID-19 using the analysis of chest X-ray images. Deeply trained CheXNet model and hybrid feature extraction techniques were applied in experiments.…”

Applying Hunger Game Search (Hgs) for Selecting Significant Blood Indicators for Early Prediction of Icu Covid-19 Severity

“…The World Health Organization (WHO) announced that reverse transcriptionpolymerase chain reaction (RT-PCR) is the standard way to diagnose COVID-19 patients [20,48] despite the misdiagnosis of some cases where a PCR may be negative and the patient has positive COVID-19 [7]. The findings of the RT-PCR test may take many hours or days to appear and are expensive [1,53]. So, many experts recommended using alternative tools that take less time with accurate results, like vital signs, blood lab tests, and chest radiological images [1,43,48,70].…”

“…Detecting the most significant indicators is an important matter that may assist doctors by using informative features rather than using noisy or messy features. There are many methods for selecting features like filters, wrappers, and embedded techniques [36,46,53]. Nowadays, meta-heuristic algorithms have been used for selecting features as an optimization problem in healthcare and many domains [28,44,54].…”