Machine Learning Models Towards Prediction of COVID and Non-COVID 19 Patients in the Hospital’s Intensive Care Units (ICU)

Abstract

The global proliferation of COVID-19 prompted research towards the virus's detection and eventual eradication. One important area of research is the use of machine learning (ML) to realize and battle COVID-19. The goal of this study is to use machine learning to monitor COVID and non-COVID-19 patients and decide whether or not to transfer them to the intensive care unit (ICU). The precise disease diagnosis was essential due to the lack of oxygen supplementation in the majority of hospitals around the world. It will improve the effectiveness of the ICU facilities and lessen the load on the medical personnel and the ICU facilities by accurately forecasting how patients will be treated. If stable patients are recognized among all patients, home treatment could be established for stable patients. In this research, three machine learning algorithms were chosen as the method used, which are K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Extra Tree Classifier. These algorithms were chosen for their simplicity and robustness and based on the conducted literature review. A dataset containing 100 ICU and 131 stable patients of Covid and non-Covid samples from 24th Moscow City State Hospital was used. By using SMOTE technique with 10-fold cross-validation and feature selection on the dataset, KNN achieved an accuracy of 94.65%, SVM with an accuracy of 94.65%, and an accuracy of 96.18% for the Extra Tree Classifier. The outcomes of this research on the selected dataset prove how accurate these algorithms were able to predict the classes