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Application of Binary Logistic Regression Model to Assess the Likelihood of Overweight

Received: 19 January 2019     Accepted: 20 February 2019     Published: 6 March 2019
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Abstract

This study attempts to assess the likelihood of overweight and associated factors among the young students by analyzing their physical measurements and physical activity index. This paper has classified four hundred and fifteen subjects and precisely estimated the likelihood of outcome overweight by combining body mass index and CUN-BAE calculated. Multicollinearity is tested with multiple regression analysis. Box-Tidwell Test is used to check the linearity of the continuous independent variables and their logit (log odds). The binary regression analysis was executed to determine the influences of gender, physical activity index, and physical measurements on the likelihood that the subjects fall in overweight category. The sensitivity and specificity described by the model are 55.9% and 96.9% respectively. The increase in the value of waist to height ratio and neck circumference and drop in physical activity index are associated with the increased likelihood of subjects falling to overweight group. The prevalence of overweight is higher (27.8%) in female than in male (14.7%) subjects. The odds ratio for gender reveals that the likelihood of subjects falling to overweight category is 2.6 times higher in female compared to male subjects.

Published in American Journal of Theoretical and Applied Statistics (Volume 8, Issue 1)
DOI 10.11648/j.ajtas.20190801.13
Page(s) 18-25
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Overweight, Waist to Height Ratio, Neck Circumference, Binary Logistic Model, Odds Ratio

References
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[9] Lancet. (2004). Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. WHO expert consultation Public Health, 363(9403), 157-163.
[10] Hung, S. P., Chen, C. Y., Guo, F. R., Chang, C. I., and Jan, C. F. (2017). Combine body mass index and body fat percentage measures to improve the accuracy of obesity screening in young adults. Obesity Research and Clinical Practice, 11(1), 11-18.
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[13] Javier, G. A., Victor, V., et al. (2012). Clinical usefulness of a new equation for estimating body fat. Diabetes Care, 35: 383-388.
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[15] Ho-Pham, L. T., Campbell, L. V., and Nguyen, T. V. (2011). More on body fat cutoff points. Mayo Clinic Proceedings, 86(6), 584. http://doi.org/10.4065/mcp.2011.0097.
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[18] Sylvia, W. S. (2004). Biostatistics and Epidemiology: a primer for health and biomedical professional (3rd ed.). USA: Springer.
[19] Kleinbaum, G. D., and Klein, M. (2010). Logistic regression: A self learning text (3rd ed.). New York: Springer.
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    Noora Shrestha. (2019). Application of Binary Logistic Regression Model to Assess the Likelihood of Overweight. American Journal of Theoretical and Applied Statistics, 8(1), 18-25. https://doi.org/10.11648/j.ajtas.20190801.13

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    ACS Style

    Noora Shrestha. Application of Binary Logistic Regression Model to Assess the Likelihood of Overweight. Am. J. Theor. Appl. Stat. 2019, 8(1), 18-25. doi: 10.11648/j.ajtas.20190801.13

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    AMA Style

    Noora Shrestha. Application of Binary Logistic Regression Model to Assess the Likelihood of Overweight. Am J Theor Appl Stat. 2019;8(1):18-25. doi: 10.11648/j.ajtas.20190801.13

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  • @article{10.11648/j.ajtas.20190801.13,
      author = {Noora Shrestha},
      title = {Application of Binary Logistic Regression Model to Assess the Likelihood of Overweight},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {8},
      number = {1},
      pages = {18-25},
      doi = {10.11648/j.ajtas.20190801.13},
      url = {https://doi.org/10.11648/j.ajtas.20190801.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20190801.13},
      abstract = {This study attempts to assess the likelihood of overweight and associated factors among the young students by analyzing their physical measurements and physical activity index. This paper has classified four hundred and fifteen subjects and precisely estimated the likelihood of outcome overweight by combining body mass index and CUN-BAE calculated. Multicollinearity is tested with multiple regression analysis. Box-Tidwell Test is used to check the linearity of the continuous independent variables and their logit (log odds). The binary regression analysis was executed to determine the influences of gender, physical activity index, and physical measurements on the likelihood that the subjects fall in overweight category. The sensitivity and specificity described by the model are 55.9% and 96.9% respectively. The increase in the value of waist to height ratio and neck circumference and drop in physical activity index are associated with the increased likelihood of subjects falling to overweight group. The prevalence of overweight is higher (27.8%) in female than in male (14.7%) subjects. The odds ratio for gender reveals that the likelihood of subjects falling to overweight category is 2.6 times higher in female compared to male subjects.},
     year = {2019}
    }
    

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    T1  - Application of Binary Logistic Regression Model to Assess the Likelihood of Overweight
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    JF  - American Journal of Theoretical and Applied Statistics
    JO  - American Journal of Theoretical and Applied Statistics
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    PB  - Science Publishing Group
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    AB  - This study attempts to assess the likelihood of overweight and associated factors among the young students by analyzing their physical measurements and physical activity index. This paper has classified four hundred and fifteen subjects and precisely estimated the likelihood of outcome overweight by combining body mass index and CUN-BAE calculated. Multicollinearity is tested with multiple regression analysis. Box-Tidwell Test is used to check the linearity of the continuous independent variables and their logit (log odds). The binary regression analysis was executed to determine the influences of gender, physical activity index, and physical measurements on the likelihood that the subjects fall in overweight category. The sensitivity and specificity described by the model are 55.9% and 96.9% respectively. The increase in the value of waist to height ratio and neck circumference and drop in physical activity index are associated with the increased likelihood of subjects falling to overweight group. The prevalence of overweight is higher (27.8%) in female than in male (14.7%) subjects. The odds ratio for gender reveals that the likelihood of subjects falling to overweight category is 2.6 times higher in female compared to male subjects.
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Author Information
  • Department of Mathematics and Statistics, P. K. Multiple Campus, Tribhuvan University, Kathmandu, Nepal

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