A Comparison Study between Regression Models for Analyzing Anemia Diseases

Taghreed Al-Said, Sanaa Al-Marzouki, Mona Adham

Abstract


Regression models are the suitable statistical techniques for drawing inferences about relationships among interrelated variables. These models are applicable in many ­fields, such as the social field, physical field, biological sciences, business and medical fields. Regression models are perhaps the most used of all data analysis methods. This research interests in comparing regression models and applying these models in analyzing two real data sets of anemia diseases.  Also, many evaluating methods are applied in the research to choose between models, determining variables that effective the anemia diseases.  The analysis of the results detects the best variables, the suitable model and the best criterion can be used with the medical data. 


Keywords


logistic regression models; anemia diseases; Iterative weighted least square methods; r-squared measure; Hosmer-Lemeshow test.

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References


A. Agresti. Categorical Data Analysis. New Jersey: John Wiley & Sons, 2007.

W. Chen, Y. Chen, and Y.M.B. Guo. "Density-based logistic regression". The National Science Foundation of USA, 2013.

C.M. Dayton. “Logistic regression analysis. Statistics & evaluation linear programming for resolving classification problem”. International Mathematical Forum, pp. 3125-3141, 1992.

G. Gregoire. “Logistic regression”. EAS Publication series, 66, pp. 89-120, 2014.

J. Gorospe, S. Bismonte, R. Areilla, and G. Gironella. "Ordinal logistic regression analyses on anemia for children aged 6 months to 5 years old in the Philippines". Presented at the De La Salle University Research Congress, 2014.

J.F. Hair, R.F. Anderson, R.L. Tatham, and W.C. Black. Multivariate Data Analysis. Internationals edition, New York, USA, Maxwell, Macmillan, 1992.

D.W. Hosmer and S. Lemeshow. Applied Logistic Regression. John Wiley & Sons Inc., 2000.

A. Krap. “Using logistic regression to predict customer retention”. Sierra Information Service, Inc., 1998.

D. Montgomery, E. Peck, and G.Vining. Introduction to Linear Regression Analysis. Fifth edition. John Wiley & Sons, Inc., 2012.

K.P. Murphy. Machine Learning-A Probabilistic Perspective. The Massachusetts Institute of Technology Press, pp. 245-259, 2012.

M. Pohar, M. Blas, S. Turk (2004)."Comparison of logistic regression and linear discriminant analysis: A Simulation Study". Metodoloski zvezki.1 (1), pp. 143 – 161, 2004.

J. Rawlings, S. Pantula, and D. Dickey. Applied Regression Analysis: A Research Tool. Second Edition, 1932.

R.L.Smith, and J.C. Naylor. “A comparison of maximum likelihood and Bayesian estimators for the three-parameter Weibull distribution”. Applied Statistics, 36, pp. 358-369, 1987.

S.H. Walker, and D.B. Duncan. "Estimation of the probability of an event as a function of several independent variables". Biometrika. 54, pp. 167–178, 1967.

H. Yusuff, N. Mohamad, U.K. Ngah, and A. Yahya.. "Brest cancer analysis using logistic regression". International Journal of Research in Engineering and Technology. 10, 1, pp. 14-22, 2012.

J. Tellinghuisen, and C.H. Bolster. “Using R^2 to compare least-squares fit models: when it must fail”. Chemometrics and intelligent laboratory systems. 105, pp. 220-222, 2011.

S. Zhou, F. Guo, L. Li, Y. Zhou, Y. Lei, Y. Hu, H. Su, X. Chen., P. Yin, and X. Jian. "Multiple logistic regression analysis of risk factors for carcinogenesis of oral submucous fibrosis in mainland China". International Journal of Oral Maxillofacial Surgery. 37, pp. 1094–1098, 2008.

Sites:

The National Heart, Lung, and Blood Institute https://www.nhlbi.nih.gov/files/docs/public/blood/anemia-inbrief_yg.pdf , 2011.

B. De- Benoist, E. McLean, I Egli, and C. Cogswell (1993-2005)

http://www.who.int/ vmnis/anaemia/prevalence/summary/anaemia_data_status_t2/en/ , 1993-2005.


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