Suggested methods for prediction using semiparametric regression function

Document Type : Research Paper


Family and Community Medicine Department, Al Kindy Medical College, University of Baghdad, Iraq


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).


[1] G. Wu, X. Liu, T. Chen, G. Xu, W. Wang X. Zeng and X. Zhang, Elevation-dependent variations of tree growth
and intrinsic water- use efficiency in Schrenk spruce ( Picea schrenkiana ) in the western Tianshan Mountains,
Front. Plant Sci. 2015.
[2] H. J. Stock and W. M. Watson, Business Cycles, Indicators and Forecasting, January 1993. http://www.nber.
[3] J. Fled, D. Tremblay, S. Thibaud and A. Kessler, Ferritin levels in patients with COVID -19: A poor predictor
of mortality and hem phagocytic lymphohistiocytosis, Int. J. Lab. Hemat. 42 (2020) 773–779.
[4] K.-W. Lee and Ch.-F. Lee, Cash holdings and corporate governance in the family-controlled firms, 1992.
[5] L. Cheng, H. Li, L. Li, Ch. Liu, S. Yan, H. Chen and Y. Li, Ferritin in the coronavirus disease 2019 ( COVID
-19): A systematic review and meta-analysis, J. Clin. Lab. Anal. 34(10) (2020) e23618.
[6] R.G. Lomax, Statistical Concepts: A Second Course, Routledge, 2018.
[7] A.S. Mohamed and N. A. Mohamed, Comparison between regression and artificial Neural Network in Forecasting,
4th Int. Sci. Conf. Arab Statist. 2013, pp: 20–21.
[8] Pan American Health Organization (WHO/PAHO), Ferritin levels and COVID-19, International Repository for
Information Sharing.
[9] S. Sawilowsky, F. S. Einstein and B. Fisher, The Probable Difference Between Two Means When σ
1 = σ
, J.
Modern Appl. Stat. Meth. 1 (2002) 461–472.
Volume 12, Issue 2
November 2021
Pages 2263-2267
  • Receive Date: 04 March 2021
  • Revise Date: 18 June 2021
  • Accept Date: 09 July 2021