Prediction of blood lead level in maternal and fetal us-ing generalized linear model

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Over the past decades, with advanced data collection techniques, a different type of data continues to appear in various biological, sciences, medical, social, and economical studies. Statistical modeling is essential in many scientific research areas because it explains the relationship between the response variable of interest and a number of explanatory variables. Generalized linear models (GLMs) are generalization of the linear regression models, which allow fitting regression models to response variable that is non normal and follows a general exponential family. The aim of this study is to encourage and initiate the application of GLMs to predict the maternal and fetal blood-lead level. The inverse Gaussian distribution with inverse quadratic link function is considered. Four main effects were significant in the prediction of the maternal blood-lead level (pica, smoking of mother, dairy products intake of mother, calcium intake of mother), while in the prediction of the fetal blood-lead level, two main effects showed significance (dairy products intake of mother and hemoglobin of mother).


  • Keywords


    Generalized Linear Models; Exponential Family; Inverse Gaussian distribution; Link Functions.

  • References


      [1] A.M. Al-Fakih, Z.Y. Algamal, M.H. Lee, H.H. Abdallah, H. Maarof, M. Aziz, Quantitative structure-activity relationship model for prediction study of corrosion inhibition efficiency using two-stage sparse multiple linear regression, Journal of Chemometrics 30(7) (2016) 361-368. https://doi.org/10.1002/cem.2800.

      [2] A.M. Al-Fakih, M. Aziz, H.H. Abdallah, Z.Y. Algamal, M.H. Lee, H. Maarof, High dimensional QSAR study of mild steel corrosion inhibition in acidic medium by furan derivatives, International Journal of Electrochemical Science 10 (2015) 3568-3583.

      [3] Z.Y. Algamal, Exponentiated exponential distribution as a failure time distribution, IRAQI Journal of Statistical science 14 (2008) 63-75.

      [4] Z.Y. Algamal, Paired Bootstrapping procedure in Gamma Regression Model using R, Journal of Basrah Researches 37(4) (2011) 201-211.

      [5] Z.Y. Algamal, Diagnostic in Poisson regression models, Electronic Journal of Applied Statistical Analysis 5(2) (2012) 178-186.

      [6] Z.Y. Algamal, Using maximum likelihood ratio test to discriminate between the inverse Gaussian and gamma distributions, International Journal of Statistical Distributions 1(1) (2017) 27-32.

      [7] Z.W. Al-Mola, Maternal and Umbilical Cord Blood Lead Levels and Pregnancy Outcomes Hospital Based Enquiry, M.Sc thesis, College of Medicine, Mosul University (2007).

      [8] Z.Y. Algamal, H.T.M. Ali, An efficient gene selection method for high-dimensional microarray data based on sparse logistic regression, Electronic Journal of Applied Statistical Analysis 10(1) (2017) 242-256.

      [9] Z.Y. Algamal, H.T.M. Ali, Bootstrapping pseudo - R2 measures for binary response variable model, Biomedical Statistics and Informatics 2(3) (2017) 107-110.

      [10] Z.Y. Algamal, M.H. Lee, Penalized logistic regression with the adaptive LASSO for gene selection in high-dimensional cancer classification, Expert Systems with Applications 42(23) (2015) 9326-9332. https://doi.org/10.1016/j.eswa.2015.08.016.

      [11] Z.Y. Algamal, M.H. Lee, Regularized logistic regression with adjusted adaptive elastic net for gene selection in high dimensional cancer classification, Computers in Biology and Medicine 67 (2015) 136-45. https://doi.org/10.1016/j.compbiomed.2015.10.008.

      [12] Z.Y. Algamal, M.H. Lee, Penalized Poisson regression model using adaptive modified elastic net penalty, Electronic Journal of Applied Statistical Analysis 8(2) (2015) 236-245.

      [13] Z.Y. Algamal, M.H. Lee, High dimensional logistic regression model using adjusted elastic net penalty, Pakistan Journal of Statistics and Operation Research 11(4) (2015) 667-676. https://doi.org/10.18187/pjsor.v11i4.990.

      [14] Z.Y. Algamal, M.H. Lee, Adjusted adaptive lasso in high-dimensional Poisson regression model, Modern Applied Science 9(4) (2015) 170-176. https://doi.org/10.5539/mas.v9n4p170.

      [15] Z.Y. Algamal, M.H. Lee, Applying penalized binary logistic regression with correlation based elastic net for variables selection, Journal of Modern Applied Statistical Methods 14(1) (2015) 168-179.

      [16] J.A. Nelder, R.W.M. Wedderburn, Generalized Linear Models, Journal of Royal Statistics 135 (1972) 370-384. https://doi.org/10.2307/2344614.

      [17] Z.Y. Algamal, M.H. Lee, A new adaptive L1-norm for optimal descriptor selection of high-dimensional QSAR classification model for anti-hepatitis C virus activity of thiourea derivatives, SAR and QSAR in Environmental Research 28(1) (2017) 75-90. https://doi.org/10.1080/1062936X.2017.1278618.

      [18] Z.Y. Algamal, M.H. Lee, A.M. Al-Fakih, High-dimensional quantitative structure-activity relationship modeling of influenza neuraminidase a/PR/8/34 (H1N1) inhibitors based on a two-stage adaptive penalized rank regression, Journal of Chemometrics 30(2) (2016) 50-57. https://doi.org/10.1002/cem.2766.

      [19] Z.Y. Algamal, M.H. Lee, A.M. Al-Fakih, M. Aziz, High-dimensional QSAR prediction of anticancer potency of imidazo[4,5-b]pyridine derivatives using adjusted adaptive LASSO, Journal of Chemometrics 29(10) (2015) 547-556. https://doi.org/10.1002/cem.2741.

      [20] Z.Y. Algamal, M.H. Lee, A.M. Al-Fakih, M. Aziz, High-dimensional QSAR modelling using penalized linear regression model with L1/2-norm, SAR and QSAR in Environmental Research 27(9) (2016) 703-19. https://doi.org/10.1080/1062936X.2016.1228696.

      [21] Z.Y. Algamal, M.H. Lee, A.M. Al-Fakih, M. Aziz, High-dimensional QSAR classification model for anti-hepatitis C virus activity of thiourea derivatives based on the sparse logistic regression model with a bridge penalty, Journal of Chemometrics (2017) e2889. https://doi.org/10.1002/cem.2889.

      [22] Z.Y. Algamal, M.K. Qasim, H.T.M. Ali, A QSAR classification model for neuraminidase inhibitors of influenza A viruses (H1N1) based on weighted penalized support vector machine, SAR and QSAR in Environmental Research (2017) 1-12. https://doi.org/10.1080/1062936X.2017.1326402.

      [23] P. De Jong, G.Z. Heller, Generalized Linear Models for Insurance Data, Cambridge University Press, UK, 2008. https://doi.org/10.1017/CBO9780511755408.

      [24] J.W. Hardin, J. Hilbe, Generalized Linear Models and Extensions, 2nd ed., Stata Press, USA, 2007.

      [25] Y. Jiao, Y. Chen, An application of Generalized Linear Models in Production Model and Sequential Population Analysis, Fisheries Research 70 (2004) 367-376. https://doi.org/10.1016/j.fishres.2004.08.027.

      [26] M.A. Kahya, W. Al-Hayani, Z.Y. Algamal, Classification of breast cancer histopathology images based on adaptive sparse support vector machine, Journal of Applied Mathematics & Bioinformatics 7(1) (2017) 49-69.

      [27] P. McCullagh, J.A. Nelder, Generalized Linear Models, 2nd ed., Chapman and Hall Inc., London, 1989. https://doi.org/10.1007/978-1-4899-3242-6.

      [28] R.H. Myers, D.C. Montgomery, G.G. Vining, Generalized Linear Models with Applications in Engineering and the Sciences, John Wiley & Sons, Inc., New York, 2002.

      [29] P. Vidoni, Prediction and Calibration in Generalized Linear Models, the Annals of Institute of Statistical Mathematics 55(1) (2003) 169-185.

      [30] C. Zhukovskaya, Use of the Generalized Linear Model in Forecasting the Air Passengers Conveyances from EU Countries, Journal of Computer Modeling and New Technologies 11(1) (2007) 62-72.

      [31] M.A. Kahya, W. Al-Hayani, Z.Y. Algamal, Classification of breast cancer histopathology images based on adaptive sparse support vector machine, Journal of Applied Mathematics & Bioinformatics 7(1) (2017) 49-69.

      [32] Z.Y. Algamal, M.K. Qasim, H.T.M. Ali, A QSAR classification model for neuraminidase inhibitors of influenza A viruses (H1N1) based on weighted penalized support vector machine, SAR and QSAR in Environmental Research (2017) 1-12.


 

View

Download

Article ID: 7615
 
DOI: 10.14419/ijasp.v5i2.7615




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.