An ICU Admission Predictive Model for COVID-19 Patients in Saudi Arabia

• Published in: International journal of advanced computer science & applications Vol. 12; no. 7
• Main Authors: Ghandorh, Hamza, Khan, Muhammad Zubair, Alsufyani, Raed, Khan, Mehshan, Alsofayan, Yousef M., Khan, Anas A., Alahmari, Ahmed A.
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 2021
• Subjects: Algorithms; Artificial neural networks; Coronaviruses; COVID-19; Disease control; Disease transmission; Error analysis; Machine learning; Mathematical models; Prediction models; Public health; Robustness (mathematics); Viral diseases
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2021.0120764

Globally, COVID-19 already emerged in around 170 million confirmed cases of infected people and, as of May 31, 2021, affected more than 3.54 million deaths. This pandemic has given rise to numerous public health and socioeconomic issues, emphasizing the significance of unraveling the epidemic’s history and forecasting the disease’s potential dynamics. A variety of mathematical models have been proposed to obtain a deeper understanding of disease transmission mechanisms. Machine Learning (ML) models have been used in the last decade to identify patterns and enhance prediction efficiency in healthcare applications. This paper proposes a model to predict COVID-19 patients admission to the intensive care unit (ICU). The model is built upon robust known classification algorithms, including classic Machine Learning Classifiers (MLCs), an Artificial Neural Network (ANN) and ensemble learning. This model’s strength in predicting COVID-19 infected patients is shown by performance analysis of various MLCs and error metrics. Among other used ML models, the ANN model resulted in the highest accuracy, 97.9% over other models. Mean Squared Error showed that the ANN method had the lowest error (0.0809). In conclusion, this paper could be beneficial to ICU staff to predict ICU admission based on COVID-19 patients’ clinical characteristics.