COVID-19 Patient Health Prediction Using Boosted Random Forest Algorithm

TLDR

AI techniques are increasingly used for real‑time data collection and prediction, and the COVID‑19 pandemic has overwhelmed healthcare systems worldwide, creating an urgent need for rapid patient outcome prediction. The study proposes a fine‑tuned Random Forest model boosted by AdaBoost to predict COVID‑19 patient outcomes. The model incorporates patients’ geographical, travel, health, and demographic data to predict case severity and outcome. The model achieved 94% accuracy and an F1 score of 0.86, and analysis showed a positive gender‑death correlation with most patients aged 20‑70.

Abstract

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 pandemic of colossal nature. Coronavirus disease 2019 (COVID-19) pandemic which originated in Wuhan China has had disastrous effects on the global community and overburdened the advanced healthcare systems in the world. Globally; over 4 063 525 confirmed cases and 282,244 deaths have been recorded as of 11th May 2020 according to European Centre for Disease Prevention and Control agency. However, the current rapid and exponential rise in the number of patients has necessitated efficient and quick prediction of the possible outcome of an infected patient for appropriate treatment using AI techniques. This paper proposes a fine-tuned Random Forest model boosted by AdaBoost algorithm. The model uses the COVID-19 patients: 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 the patients' gender and deaths and also indicates that the majority of patients are in the age range of 20-70 years