Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: A review - Alballa, Norah; Al-Turaiki, Isra (Informatics in medicine unlocked, 2021)
The existence of widespread COVID-19 infections has prompted worldwide efforts to control and manage the virus, and hopefully curb it completely. One important line of research is the use of machine learning (ML) to understand and fight COVID-19. This is currently an active research field. Although there are already many surveys in the literature, there is a need to keep up with the rapidly growing number of publications on COVID-19-related applications of ML. This paper presents a review of recent reports on ML algorithms used in relation to COVID-19. We focus on the potential of ML for two main applications: diagnosis of COVID-19 and prediction of mortality risk and severity, using readily available clinical and laboratory data. Aspects related to algorithm types, training data sets, and feature selection are discussed. As we cover work published between January 2020 and January 2021, a few key points have come to light. The bulk of the machine learning algorithms used in these two applications are supervised learning algorithms. The established models are yet to be used in real-world implementations, and much of the associated research is experimental. The diagnostic and prognostic features discovered by ML models are consistent with results presented in the medical literature. A limitation of the existing applications is the use of imbalanced data sets that are prone to selection bias. •Machine learning is used for COVID-19 diagnosis and prediction of mortality/severity.•Supervised learning is most often used for diagnostic and prognostic prediction.•Imbalanced data sets and selection bias are limitations of existing COVID-19 data.•The results of machine learning models are consistent with those of medical studies.