Suggested methods for prediction using semiparametric regression function

Document Type : Research Paper

Author
Aseel Sameer Mohamed
Family and Community Medicine Department, Al Kindy Medical College, University of Baghdad, Iraq
10.22075/ijnaa.2021.5373
Abstract
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).
Keywords
Semi-parametric method
Neural Network models (NN)
regression
Ferritin level
COVID 19
multilayer perceptron (MLP)
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International Journal of Nonlinear Analysis and Applications
Volume 12, Issue 2
November 2021
Pages 2263-2267

  • Receive Date 04 March 2021
  • Revise Date 18 June 2021
  • Accept Date 09 July 2021

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