Objectives: Coronavirus Disease 2019 (COVID-19) has become a pandemic outbreak . Risk stratification at hospital admission is of vital importance for medical decision making and resource allocation . There is no sophisticated tool for this purpose . This study aimed to develop neural network models with predictors selected by genetic algorithms (GA).
Methods: This study was conducted in Wuhan Third Hospital from January 2020 to March 2020 . Predictors were collected on day 1 of hospital admission . The primary outcome was the vital status at hospital discharge . Predictors were selected by using GA, and neural network models were built with the cross-validation method . The final neural network models were compared with conventional logistic regression models .
Results: A total of 246 patients with COVID-19 were included for analysis . The mortality rate was 17.1% (42/246). Non-survivors were significantly older (median (IQR): 69 (57 , 77) vs. 55 (41 , 63) years; p <0.001), had higher high-sensitive troponin I (0.03 (0 , 0.06) vs. 0 (0 , 0.01) ng/L; p <0.001), C-reactive protein (85.75 (57.39 , 164.65) vs. 23.49 (10.1 , 53.59) mg/L; p <0.001), D-dimer (0.99 (0.44 , 2.96) vs. 0.52 (0.26 , 0.96) mg/L; p <0.001), and α-hydroxybutyrate dehydrogenase (306.5 (268.75 , 377.25) vs. 194.5 (160.75 , 247.5); p <0.001) and a lower level of lymphocyte count (0.74 (0.41 , 0.96) vs. 0.98 (0.77 , 1.26) × 109/L; p <0.001) than survivors . The GA identified a 9-variable (NNet1) and a 32-variable model (NNet2). The NNet1 model was parsimonious with a cost on accuracy; the NNet2 model had the maximum accuracy . NNet1 (AUC : 0.806; 95% CI [0.693–0.919] ) and NNet2 (AUC : 0.922; 95% CI [0.859–0.985] ) outperformed the linear regression models .
Conclusions: Our study included a cohort of COVID-19 patients . Several risk factors were identified considering both clinical and statistical significance . We further developed two neural network models, with the variables selected by using GA . The model performs much better than the conventional generalized linear models.