suggested methods for prediction using semiparametric regression function

Ferritin is a key organizer of protected deregulation, particularly below risky hyperferritinemia, by straight immune-suppressive and pro-inflammatory things.  we conclude that there is a significant association between levels of ferritin and the harshness of covid-19. in this paper, we introduce a semi-parametric method for prediction by making a combination of nn and regression models. so, two methodologies are adopted, neural network (nn) and regression model in designing the model; the data was collected from a nursing home hospital for period 11/7/2021- 23/7/2021, the sample size is 100 covid positive patients with 12 females 38 males out of 50, while 26 female 24 male are non-covid out of 50. the input variables of the nn model are identified as the ferritin and a gender variable. the higher results precision is attained by the multilayer perceptron (mlp) networks when we applied the explanatory variables as the inputs with one hidden layer, which covers 3 neurons, as the planned many hidden layers are with one output of the fitting nn model which is used in stages of training and validation beside the actual data. we used a portion of the actual data to verify the behavior of the developed models, we find out that only one observation is a false predictive value. this means that the estimation model has significant parameters to forecast the type of covid cases (covid or no covid).