Machine learning predictive model for severe COVID-19.
Journal:
Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases
Published Date:
Jan 28, 2021
Abstract
To develop a modified predictive model for severe COVID-19 in people infected with Sars-Cov-2. We developed the predictive model for severe patients of COVID-19 based on the clinical date from the Tumor Center of Union Hospital affiliated with Tongji Medical College, China. A total of 151 cases from Jan. 26 to Mar. 20, 2020, were included. Then we followed 5 steps to predict and evaluate the model: data preprocessing, data splitting, feature selection, model building, prevention of overfitting, and Evaluation, and combined with artificial neural network algorithms. We processed the results in the 5 steps. In feature selection, ALB showed a strong negative correlation (r = 0.771, P < 0.001) whereas GLB (r = 0.661, P < 0.001) and BUN (r = 0.714, P < 0.001) showed a strong positive correlation with severity of COVID-19. TensorFlow was subsequently applied to develop a neural network model. The model achieved good prediction performance, with an area under the curve value of 0.953(0.889-0.982). Our results showed its outstanding performance in prediction. GLB and BUN may be two risk factors for severe COVID-19. Our findings could be of great benefit in the future treatment of patients with COVID-19 and will help to improve the quality of care in the long term. This model has great significance to rationalize early clinical interventions and improve the cure rate.