Applying the Binary Logistic Regression Analysis on The Medical Data
DOI:
https://doi.org/10.25271/2017.5.4.388Keywords:
Logistic regression, Hosmer–Lemeshow test, Likelihood ratio test, Maximum likelihood estimation, Wald testAbstract
In this paper, the Binary Logistic Regression Analysis BLRA technique has been used and applied for building the best model for Hepatitis disease data using best subsets regression and stepwise procedures and depending on some laboratory tests such as glutamate oxalate transaminase, glutamate pyruvate transaminase, alkaline phosphatase, and total serum bilirubin which represents explanatory variables. Also, the technique has used for classifying persons into two groups which are infected and non-infected with viral Hepatitis disease. A random sample size consists of 200 persons has been selected which represents 86 of uninfected and 114 of infected persons. The results of the analysis showed that first, the two procedures identified the same three explanatory variables out of four and they were statistically significant, and it has been reliable in building the logistic model. And second, the percentage of visible correct classification rate was about 98% which represents the high ability of the model for classification.
References
Abdullah E., &, Majid E. (2014). A comparative study between Linear discriminant analysis and multinomial logistic regression. An - Najah University Journal Research (Humanities), 28: 1525-1548.
Amir W, Mamat M, & Ali Z.(2014). Association of hypertension with risk factors using logistic regression. Applied Mathematical Sciences, 8, 2563 – 2572.
Bergerud W. (1996). Introduction to logistic regression models: With worked forestry examples. Biometrics Information Handbook, British Columbia, 1996.
Bewick V., Cheek L., & Ball J. (2005). Statistics review 14: Logistic regression. Critical Care, 9: 112-118.
Burns RB., & Burns RA. (2008). Business research methods and statistics using SPSS: Sage Publishing LTD.
Hair J., Black W., Babin B., & Anderson R. (2010). Multivariate data analysis, Seventh Edition.: Pearson Prentice Hall.
Hosmer D., & Lemeshow S. (2000). Applied logistic regression. Second Edition: John Wiley and Sons, Inc.
Iehab A. M, & Sahar H. A.(2013). Choosing the best formula for the multiple linear regression model. Magazine of College Administration and Economics for Economic, Babylon University, 242: 119-146.
James G., Witten D., Hastie T., & Tibshirani R. (2013). An Introduction to statistical learning: with applications in R: Springer.
Javali S., & Pandit P. (2012). Multiple logistic regression model to predict risk factors of oral health diseases. Romanian Statistical Review Journal, 73-86.
Junguk H., Eun-Hee C., Kwang-Hyun B., & Kyung J. L. (2017). Prediction of gestational diabetes mellitus by unconjugated estriol levels in maternal serum. International Journal of Medical Sciences. 14, 123-127.
Kleinbaum D., & Klein M. (2010). Logistic regression: A self‐learning text. Third Edition.: Springer.
Lawless J. F., & Singhal K. (1987). ISMOD: An all- subsets regression program for generalized linear models, I. Statistical and computational background. Computer methods and programs in biomedicine, 24, 117-124.
Mythili T., Dev Mukherji M, Padalia N, & Naidu A. A heart disease prediction model using SVM-decision trees-logistic regression (SDL). International Journal of Computer Applications, 68,11-14.
Nagelkerke N. (1991). A note on a general definition of the coefficient of determination. Biometrika, 78: 691-692.
Peng C., Lee K., & Ingersoll G. (2002). An Introduction to logistic regression analysis and reporting. The Journal of Educational Research, 96: 3-15.
Qais M. (2015). Comparison of discriminant analysis and logistic regression analysis: An application on caesarean births and natural births data. Journal of The Institute of Natural and Applied Sciences, 20, 34-46.
Reeda P., & Wub Y. (2013). Logistic regression for risk factor modelling in stuttering research. Journal of Fluency Disorders, 38, 88-101.
Rencher A. (2002). Methods of multivariate analysis. Second Edition: A John Wiley and Sons, Inc. Publication.
Sarkar S. K., Midi H., & Rana S. (2010). Model selection in logistic regression and performance of its predictive ability. Australian Journal of Basic and Applied Science, 12, 5813-5822.
StatSoft. STATISTICA.(2013). Formula guide: Logistic Regression, Version 1.1. www.statsoft.com.
Sweet S., & Martin K. (2011). Data analysis with SPSS: A first course in applied statistics. Fourth Edition.: Pearson publisher.
Vaitheeswaran K., Subbiah M., Ramakrishnan R., & Kannan T. (2016). A comparison of ordinal logistic regression models using Classical and Bayesian approaches in an analysis of factors associated with diabetic retinopathy. Journal of Applied Statistics, 43, 2254-2260.
Zhao, L., Chen, Y., & Schaffner, D.W. (2001). Comparison of logistic regression and linear regression in modeling percentage data. Applied and Environmental Microbiology, 67, 2129-2135.
Amir W, Mamat M, & Ali Z.(2014). Association of hypertension with risk factors using logistic regression. Applied Mathematical Sciences, 8, 2563 – 2572.
Bergerud W. (1996). Introduction to logistic regression models: With worked forestry examples. Biometrics Information Handbook, British Columbia, 1996.
Bewick V., Cheek L., & Ball J. (2005). Statistics review 14: Logistic regression. Critical Care, 9: 112-118.
Burns RB., & Burns RA. (2008). Business research methods and statistics using SPSS: Sage Publishing LTD.
Hair J., Black W., Babin B., & Anderson R. (2010). Multivariate data analysis, Seventh Edition.: Pearson Prentice Hall.
Hosmer D., & Lemeshow S. (2000). Applied logistic regression. Second Edition: John Wiley and Sons, Inc.
Iehab A. M, & Sahar H. A.(2013). Choosing the best formula for the multiple linear regression model. Magazine of College Administration and Economics for Economic, Babylon University, 242: 119-146.
James G., Witten D., Hastie T., & Tibshirani R. (2013). An Introduction to statistical learning: with applications in R: Springer.
Javali S., & Pandit P. (2012). Multiple logistic regression model to predict risk factors of oral health diseases. Romanian Statistical Review Journal, 73-86.
Junguk H., Eun-Hee C., Kwang-Hyun B., & Kyung J. L. (2017). Prediction of gestational diabetes mellitus by unconjugated estriol levels in maternal serum. International Journal of Medical Sciences. 14, 123-127.
Kleinbaum D., & Klein M. (2010). Logistic regression: A self‐learning text. Third Edition.: Springer.
Lawless J. F., & Singhal K. (1987). ISMOD: An all- subsets regression program for generalized linear models, I. Statistical and computational background. Computer methods and programs in biomedicine, 24, 117-124.
Mythili T., Dev Mukherji M, Padalia N, & Naidu A. A heart disease prediction model using SVM-decision trees-logistic regression (SDL). International Journal of Computer Applications, 68,11-14.
Nagelkerke N. (1991). A note on a general definition of the coefficient of determination. Biometrika, 78: 691-692.
Peng C., Lee K., & Ingersoll G. (2002). An Introduction to logistic regression analysis and reporting. The Journal of Educational Research, 96: 3-15.
Qais M. (2015). Comparison of discriminant analysis and logistic regression analysis: An application on caesarean births and natural births data. Journal of The Institute of Natural and Applied Sciences, 20, 34-46.
Reeda P., & Wub Y. (2013). Logistic regression for risk factor modelling in stuttering research. Journal of Fluency Disorders, 38, 88-101.
Rencher A. (2002). Methods of multivariate analysis. Second Edition: A John Wiley and Sons, Inc. Publication.
Sarkar S. K., Midi H., & Rana S. (2010). Model selection in logistic regression and performance of its predictive ability. Australian Journal of Basic and Applied Science, 12, 5813-5822.
StatSoft. STATISTICA.(2013). Formula guide: Logistic Regression, Version 1.1. www.statsoft.com.
Sweet S., & Martin K. (2011). Data analysis with SPSS: A first course in applied statistics. Fourth Edition.: Pearson publisher.
Vaitheeswaran K., Subbiah M., Ramakrishnan R., & Kannan T. (2016). A comparison of ordinal logistic regression models using Classical and Bayesian approaches in an analysis of factors associated with diabetic retinopathy. Journal of Applied Statistics, 43, 2254-2260.
Zhao, L., Chen, Y., & Schaffner, D.W. (2001). Comparison of logistic regression and linear regression in modeling percentage data. Applied and Environmental Microbiology, 67, 2129-2135.
Downloads
Published
2017-12-30
How to Cite
Abdulqader, Q. M. (2017). Applying the Binary Logistic Regression Analysis on The Medical Data. Science Journal of University of Zakho, 5(4), 330–334. https://doi.org/10.25271/2017.5.4.388
Issue
Section
Science Journal of University of Zakho
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License [CC BY-NC-SA 4.0] that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work, with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online.
