Abstract
The fast and seemingly uncontrollable spread of the novel coronavirus disease (COVID-19) poses great challenges to an already overloaded health system worldwide. It thus exemplifies an urgent need for fast and effective triage. Such triage can help in the implementation of the necessary measures to prevent patient deterioration and conserve strained hospital resources. We examine two types of machine learning models, a multilayer perceptron artificial neural networks and decision trees, to predict the severity level of illness for patients diagnosed with COVID-19, based on their medical history and laboratory test results. In addition, we combine the machine learning models with a LIME-based explainable model to provide explainability of the model prediction. Our experimental results indicate that the model can achieve up to 80% prediction accuracy for the dataset we used. Finally, we integrate the explainable machine learning models into a mobile application to enable the usage of the proposed models by medical staff worldwide.
Highlights
On 30 January 2020, the World Health Organization (WHO) declared the COVID-19 pandemic to be a public health emergency of international concern
We introduce two types of machine learning models, a multilayer perceptron (MLP) artificial neural networks (ANN) [4] and Random Forest (RF) decision trees [20], to predict the level of severity of illness in patients diagnosed with COVID-19
We summarize the contribution of this study as follows: 1. We introduce MLP and decision tree machine learning models to predict the level of severity of illness in patients diagnosed with COVID-19
Summary
On 30 January 2020, the World Health Organization (WHO) declared the COVID-19 pandemic to be a public health emergency of international concern. Very few studies [19] have leveraged machine learning to systematically explore risk factors to assess the likely severity of disease in patients diagnosed with COVID-19 and to predict patient outcomes based on early clinical data. We introduce two types of machine learning models, a multilayer perceptron (MLP) artificial neural networks (ANN) [4] and Random Forest (RF) decision trees [20], to predict the level of severity of illness in patients diagnosed with COVID-19. The rest of this paper is organized as follows: Section 2 reviews prior related works, Section 3 describes the dataset of patients diagnosed with COVID-19 that we used for our study, Section 3 presents our machine learning model, Section 4 introduces the explainability method that has been combined with our MLP model, Section 5 presents our mobile application design, and Section 6 summarizes our conclusions and suggestions for future studies
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
