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. 2021 Apr:20:100178.
doi: 10.1016/j.smhl.2020.100178. Epub 2021 Jan 16.

Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making

Mohammad Pourhomayoun  1 , Mahdi Shakibi  1
Affiliations

Predicting mortality risk in patients with COVID-19 using machine learning to help medical decision-making

Mohammad Pourhomayoun et al. Smart Health (Amst). 2021 Apr.

Abstract

In the wake of COVID-19 disease, caused by the SARS-CoV-2 virus, we designed and developed a predictive model based on Artificial Intelligence (AI) and Machine Learning algorithms to determine the health risk and predict the mortality risk of patients with COVID-19. In this study, we used a dataset of more than 2,670,000 laboratory-confirmed COVID-19 patients from 146 countries around the world including 307,382 labeled samples. This study proposes an AI model to help hospitals and medical facilities decide who needs to get attention first, who has higher priority to be hospitalized, triage patients when the system is overwhelmed by overcrowding, and eliminate delays in providing the necessary care. The results demonstrate 89.98% overall accuracy in predicting the mortality rate. We used several machine learning algorithms including Support Vector Machine (SVM), Artificial Neural Networks, Random Forest, Decision Tree, Logistic Regression, and K-Nearest Neighbor (KNN) to predict the mortality rate in patients with COVID-19. In this study, the most alarming symptoms and features were also identified. Finally, we used a separate dataset of COVID-19 patients to evaluate our developed model accuracy, and used confusion matrix to make an in-depth analysis of our classifiers and calculate the sensitivity and specificity of our model.

Keywords: COVID-19; Coronavirus; Data analytics; Machine learning; Predictive analytics.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
High-level system architecture.
Fig. 2
Fig. 2
Feature Selection: (a)Wrapper method, (b)Filter method.
Fig. 3
Fig. 3
(a) Correlation heatmap for the most correlated features to the mortality risk: (a-I) Chronic diseases (pre-existing conditions); (a-II) Symptoms. (b) Correlation heatmap for the correlation between features: (b-I) Chronic diseases (pre-existing conditions); (b-II) Symptoms.
Fig. 4
Fig. 4
ROC Curve comparison for all algorithms.
Fig. 5
Fig. 5
Neural Network confusion matrix for mortality prediction.
Fig. 6
Fig. 6
Sample results for predicting the risk of mortality (the probability of death).
See this image and copyright information in PMC

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