Machine learning predictive model for severe COVID-19

• Published in: Infection, genetics and evolution Vol. 90; p. 104737
• Main Authors: Kang, Jianhong, Chen, Ting, Luo, Honghe, Luo, Yifeng, Du, Guipeng, Jiming-Yang, Mia
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.06.2021; Published by Elsevier B.V
• Subjects: Adolescent; Adult; Aged; Aged, 80 and over; Algorithms; Biomarkers; COVID-19 - diagnosis; COVID-19 - epidemiology; COVID-19 - virology; Databases, Factual; Female; Humans; Machine Learning; Male; Middle Aged; Models, Theoretical; Predictive model; Prognosis; Research Paper; ROC Curve; SARS-CoV-2; Severe COVID-19; Severity of Illness Index; Software; Tomography, X-Ray Computed; Young Adult; CD; Severe COVID-19; Machine learning; Predictive model; ROC; BUN; ReLU; AUC
• ISSN: 1567-1348; 1567-7257
• DOI: 10.1016/j.meegid.2021.104737

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. •The model has good performance in forecasting accuracy.•We find that a low albumin, a high globulin and a high blood urea nitrogen maybe potential risk factors severe COVID-19.•The results of our study will prove helpful in the prevention of severe COVID-19.