COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm; IEEE Sweden Board member and others Analysis

Integration of artificial intelligence (AI) techniques in wireless infrastructure, real-time collection, and processing of end-user devices is now in high demand. It is now superlative to use AI to detect and predict pandemics of a colossal nature. The Coronavirus disease 2019 (COVID-19) pandemic, which originated in Wuhan China, has had disastrous effects on the global community and has overburdened advanced healthcare systems throughout the world. Globally; over 4,063,525 confirmed cases and 282,244 deaths have been recorded as of 11th May 2020, according to the European Centre for Disease Prevention and Control agency


In a paper published in top SCI journal. SMIEEE Dr. Iwendi et al proposed a fine-tuned Random Forest model boosted by the AdaBoost algorithm. Their model uses the COVID-19 patient’s geographical, travel, health, and demographic data to predict the severity of the case and the possible outcome, recovery, or death. The model has an accuracy of 94% and a F1 Score of 0.86 on the dataset used. The data analysis reveals a positive correlation between patients’ gender and deaths, and also indicates that the majority of patients are aged between 20 and 70 years

Their contributions include:

  • Processing of healthcare and travel data using machine learning algorithms in place of the traditional healthcare system to identify COVID infected person.
  • Their work compared multiple algorithms that are available for processing patient data and identified the Boosted Random Forest as the best method for processing data. Further, it executed a grid search to fine-tune the hyper parameters of the Boosted Random Forest algorithm to improve performance.
  • Their work obliterates the need to re-compare existing algorithms for processing COVID-19 patient data.
  • The authors believed that this work will enable researchers to further work on developing a solution that combines the processing of patient demographics, travel, and subjective health data with image data (scans) for better prediction of COVID-19 patient health outcomes. The full paper is found here 

Main Author

IEEE Sweden Board Member and SMIEEE Dr. Celestine Iwendi is the Key author of this publication. His research focuses on Wireless Sensor Networks, Cybersecurity, Security of Things (SoT), Machine Learning, Artificial Intelligence, Communication Controls, Internet of Things (IoT), 5G Networks and Low Power Communication Protocols.

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