Prediction of COVID-19 Patients Recovery using Ensemble Machine Learning and Vital Signs Data Collected by Novel Wearable Device

Abstract

During the spread of a pandemic such as COVID-19, the effort required of health institutions increases dramatically. Generally, Health systems' response and efficiency depend on monitoring vital signs such as blood oxygen level, heartbeat, and body temperature. At the same time, remote health monitoring and wearable health technologies have revolutionized the concept of effective healthcare provision from a distance. However, analyzing such a large amount of medical data in time to provide the decision-makers with necessary health procedures is still a challenge. In this research, a wearable device and monitoring system are developed to collect real data from more than 400 COVID-19 patients. Based on this data, three classifiers are implemented using two ensemble classification techniques (Adaptive Boosting and Adaptive Random Forest). The analysis of collected data showed a remarkable relationship between the patient's age and chronic disease on the one hand and the speed of recovery on the other. The experimental results indicate a highly accurate performance for Adaptive Boosting classifiers, reaching 99%, while the Adaptive Random Forest got a 91% accuracy metric.

COVID-19 Disease Detection based on X-Ray Image Classification using CNN with GEV Activation Function

“…There was a 91.99 percent success rate for the NASNet, MobileNet, and DenseNet. Hasan K. Naji, Hayder K, Fatlawi, Ammar J [23] implements classifiers using both ensemble classification algorithms (Adaptive Boosting and Adaptive Random Forest). The study of the data revealed a striking correlation between the patient's age, the presence of a chronic illness, and the rate of recovery.…”