Skip to main content

Advertisement

Springer Nature Link
Log in

Comparison of Artificial Neural Networks with Logistic Regression for Detection of Obesity

  • ORIGINAL PAPER
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Obesity is a common problem in nutrition, both in the developed and developing countries. The aim of this study was to classify obesity by artificial neural networks and logistic regression. This cross-sectional study comprised of 414 healthy military personnel in southern Iran. All subjects completed questionnaires on their socio-economic status and their anthropometric measures were measured by a trained nurse. Classification of obesity was done by artificial neural networks and logistic regression. The mean age±SD of participants was 34.4 ± 7.5 years. A total of 187 (45.2%) were obese. In regard to logistic regression and neural networks the respective values were 80.2% and 81.2% when correctly classified, 80.2 and 79.7 for sensitivity and 81.9 and 83.7 for specificity; while the area under Receiver-Operating Characteristic (ROC) curve were 0.888 and 0.884 and the Kappa statistic were 0.600 and 0.629 for logistic regression and neural networks model respectively. We conclude that the neural networks and logistic regression both were good classifier for obesity detection but they were not significantly different in classification.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Ayatollahi, S. M. T., and Heydari, S. T., Obesity indices among infants and their parents, Shiraz, Iran. Iranian Journal of Medical Sciences 29:161–167, 2004.

    Google Scholar 

  2. Li, Z., Bowerman, S., and Heber, D., Health ramifications of the obesity epidemic. The Surgical Clinics of North America 85:681–701, 2005.

    Article  Google Scholar 

  3. Rashidy-Pour, A., Malek, M., Eskandarian, R., and Ghorbani, R., Obesity in the Iranian population. Obesity Reviews 10:2–6, 2009.

    Article  Google Scholar 

  4. Schokker, D. F., Visscher, T. L., Nooyens, A. C., van Baak, M. A., and Seidell, J. C., Prevalence of overweight and obesity in the Netherlands. Obesity Reviews 8:101–108, 2007.

    Article  Google Scholar 

  5. Wang, Y., and Lobstein, T., Worldwide trends in childhood overweight and obesity. International Journal of Pediatric Obesity 1:11–25, 2006.

    Article  Google Scholar 

  6. Heydari, S. T., Vakili, M. A., and Emamghorashi, F., Longitudinal pattern and reference values of obesity indices of infants in Jahrom (Southern Region of), Iran. Iranian Journal of Pediatrics 18:38–46, 2008.

    Google Scholar 

  7. Organization W.H., Obesity and overweight. In 2006.

  8. Haykin, S., Neural networks: a comprehensive foundation. Upper Saddle River, New Jersey, 1999.

    MATH  Google Scholar 

  9. Ergün, U., The classification of obesity disease in logistic regression and neural network methods. Journal of Medical Systems 33:67–72, 2009.

    Article  Google Scholar 

  10. Cameron, N., The measurement of human growth. Routledge Kegan & Paul, 1984.

  11. American College of Sports Medicine. ACSM’s Guidelines for Exercise Testing and Prescription. Lippincott Williams and Wilkins, 2005.

  12. Hosmer, D., and Lemeshow, S., Applied logistic regression. Wiley-Interscience, 2000.

  13. Banerji, A., Rudders, S. A., Corel, B., Garth, A. P., Clark, S., and Camargo, C. A., Jr., Predictors of hospital admission for food-related allergic reactions that present to the emergency department. Annals of Allergy, Asthma & Immunology 106:42–48, 2011.

    Article  Google Scholar 

  14. Dong, X., Zhang, B., Kang, M. X., Chen, Y., Guo, Q. Q., Wu, Y. L., Analysis of Pancreatic Fistula According to the International Study Group on Pancreatic Fistula Classification Scheme for 294 Patients Who Underwent Pancreaticoduodenectomy in a Single Center. Pancreas, 2010.

  15. Goycochea-Robles, M. V., Sanin, L. H., Moreno-Montoya, J., et al., Validity of the COPCORD core questionnaire as a classification tool for rheumatic diseases. The Journal of Rheumatology. Supplement 86:31–35, 2011.

    Article  Google Scholar 

  16. Hasenbring, M. I., Kreddig, N., Deges G. et al., Psychological impact of genetic counseling for hereditary nonpolyposis colorectal cancer: The role of cancer history, gender, age, and psychological distress. Genet. Test Mol. Biomarkers, 2010.

  17. Dreyfus, G., Neural networks: methodology and applications. Springer Verlag, 2005.

  18. Mobley, B., Schechter, E., Moore, W., McKee, P., and Eichner, J., Predictions of coronary artery stenosis by artificial neural network. Artificial Intelligence in Medicine 18:187–203, 2000.

    Article  Google Scholar 

  19. Dreiseitl, S., and Ohno-Machado, L., Logistic regression and artificial neural network classification models: a methodology review. Journal of Biomedical Informatics 35:352–359, 2002.

    Article  Google Scholar 

  20. Adnan, M., Husain, W., Damanhoori, F., A survey on utilization of data mining for childhood obesity prediction. In IEEE, 2010, 1–6.

  21. Valavanis, I., Mougiakakou, S., Grimaldi, K., and Nikita, K., A multifactorial analysis of obesity as CVD risk factor: use of neural network based methods in a nutrigenetics context. BMC Bioinformatics 11:453, 2010.

    Article  Google Scholar 

  22. Zhang, S., Tjortjis, C., Zeng, X., Qiao, H., Buchan, I., and Keane, J., Comparing data mining methods with logistic regression in childhood obesity prediction. Information Systems Frontiers 11:449–460, 2009.

    Article  Google Scholar 

  23. Kazemnejad, A., Batvandi, Z., and Faradmal, J., Comparison of artificial neural network and binary logistic regression for determination of impaired glucose tolerance/diabetes. Eastern Mediterranean Health Journal 16:615–620, 2010.

    Google Scholar 

  24. Hart, A., and Wyatt, J., Evaluating black-boxes as medical decision aids: issues arising from a study of neural networks. Informatics for Health & Social Care 15:229–236, 1990.

    Article  Google Scholar 

  25. Llorca, J., Dierssen-Sotos, T., Gomez-Acebo, I., Gonzalez-Castro, A., and Minambres, E., Artificial neural networks predict mortality after lung transplantation better than logistic regression. The Journal of Heart and Lung Transplantation 28:1237–1238, 2009.

    Article  Google Scholar 

  26. Bioelectrical impedance analysis in body composition measurement: National Institutes of Health Technology Assessment Conference Statement. Am. J. Clin. Nutr. 64:524S–532S, 1996.

  27. Bourdès, V. S., Bonnevay, S., Lisboa, P. J. et al., Breast cancer predictions by neural networks analysis: a comparison with logistic regression. Conference proceedings : … Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 2007, 2007, 5424–5427.

  28. Shadabi, F., and Sharma, D., Comparison of artificial neural networks with logistic regression in prediction of kidney transplant outcomes. In IEEE, 2009, 543–547.

Download references

Author information

Authors and Affiliations

  1. Department of Biostatistics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, IR, Iran

    Seyed Taghi Heydari, Seyed Mohammad Taghi Ayatollahi & Najaf Zare

Authors
  1. Seyed Taghi Heydari
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Seyed Mohammad Taghi Ayatollahi
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Najaf Zare
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Seyed Mohammad Taghi Ayatollahi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Heydari, S.T., Ayatollahi, S.M.T. & Zare, N. Comparison of Artificial Neural Networks with Logistic Regression for Detection of Obesity. J Med Syst 36, 2449–2454 (2012). https://doi.org/10.1007/s10916-011-9711-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10916-011-9711-4

Keywords