[go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

ANN Prediction of Metabolic Syndrome: a Complex Puzzle that will be Completed

  • Transactional Processing Systems
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

The diagnosis of metabolic syndrome (MetS) has a leading role in the early prevention of chronic disease, such as cardiovascular disease, type 2 diabetes, cancers and chronic kidney disease. It would be very greatful that MetS diagnosis can be predicted in everyday clinical practice. This paper presents artificial neural network (ANN) prediction of the diagnosis of MetS that includes solely non-invasive, low-cost and easily-obtained diagnostic methods. This solution can extract the risky persons and suggests complete tests only on them by saving money and time. ANN input vectors are very simple and contain solely non-invasive, low-cost and easily-obtained parameters: gender, age, body mass index, waist-to-height ratio, systolic and diastolic blood pressures. ANN output is M e t S-coefficient in true/false form, obtained from MetS definition of International Diabetes Federation (IDF). ANN training, validation and testing are conducted on the large dataset that includes 2928 persons. Feed-forward ANNs with 1–100 hidden neurons were considered and an optimal architecture were determinated. Comparison with other authors leads to the conclusion that our solution achieves the highest positive predictive value P P V = 0.8579. Further, obtained negative predictive value N P V = 0.8319 is also high and close to PPV, which means that our ANN solution is suitable both for positive and negative MetS prediction.

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

Similar content being viewed by others

References

  1. Mottillo, S., Filion, K. B., Genest, J., et al., The metabolic syndrome and cardiovascular risk a systematic review and meta-analysis. J. Am. Coll. Cardiol. 56:1113–1132, 2010.

    Article  PubMed  Google Scholar 

  2. Ford, E. S., Li, C., Sattar, N., Metabolic syndrome and incident diabetes: current stateof the evidence. Diabetes Care 31:1898–1904, 2008.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Esposito, K., Chiodini, P., Colao, A., Lenzi, A., Giugliano, D., Metabolic syndrome and risk of cancer: a systematic review and metaanalysis. Diabetes Care 35:2402–2411, 2012.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Chen, J., Muntner, P., Hamm, L. L., et al., The metabolic syndrome and chronic kidney disease in US adults. Ann. Intern. Med. 140:167–174, 2004.

    Article  PubMed  Google Scholar 

  5. Hivert, M. F., Grant, R. W., Shrader, P., Meigs, J. B., Identifying primary care patients at risk for future diabetes and cardiovascular disease using electronic health records. BMC Health Serv. Res. 9:170, 2009.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Rao, D. P., Dai, S., Lagace, C., Krewski, D., Metabolic syndrome and chronic disease. Chronic Diseases and Injuries in Canada 34:36–45, 2014.

    CAS  PubMed  Google Scholar 

  7. World Health Organization: Defi nition, diagnosis and classifi cation of diabetes mellitus and its complications. Report of a WHO consultation (1999)

  8. Executive summary of the Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III). JAMA 285: 2486–2497, 2001.

  9. Balkau, B., and Charles, M. A., Comment on the provisional report from the WHO consultation. Diabetic Medicine 16:442–443, 1999.

    Article  CAS  PubMed  Google Scholar 

  10. International Diabetes Federation: The IDF consensus worldwide definition of the metabolic syndrome (2006). http://www.idf.org/metabolic-syndrome

  11. Misra, A., and Vikram, N. K., Clinical and patophysiological consequences of abdominal adiposity and abdominal adipose tissue depots. Nutrition 19:457–466, 2003.

    Article  PubMed  Google Scholar 

  12. Berg, A. H., and Scherer, P. E., Adipose tissue, inflammation, and cardiovascular disease. Circ. Res. 96: 939–949, 2005.

    Article  CAS  PubMed  Google Scholar 

  13. Stokić, E., Tomić-Naglić, D., Derić, M., Jorga, J., Therapeutic options for treatment of cardiometabolic risk. Med. Pregl. 62(Suppl 3):54–58, 2009.

    PubMed  Google Scholar 

  14. Stokić, E., Srdić Galić, B., Kupusinac, A., Doroslovački, R., Estimating SAD low-limits for the adverse metabolic profile by using artificial neural networks. TEM J. 2:115–119, 2013.

    Google Scholar 

  15. Strategy for prevention and control of chronic non-infectious diseases of the Republic of Serbia. Avaliable on: http://www.minzdravlja.info/downloads/Zakoni/Strategije/Strategija%20Za%20Prevenciju%20I%20Kontrolu%20Hronicnih%20Nezaraznih%20Bolesti.pdf [in Serbian]

  16. Grujić, V., Martinov-Cvejin, M., Ač-Nikolić, E., Nićiforović-Šurković, O.: Epidemiology of obesity in adult population of vojvodina, Vol. LVIII. [in Serbian] (2005)

  17. Moein, S.: Medical diagnosis using artificial neural networks. IGI global (2014)

  18. Lin, C. C., Bai, Y. M., Chen, J. Y., Hwang, T. J., Chen, T. T., Chiu, H. W., Li, Y. C., Low-Cost Identification of metabolic syndrome in patients treated with Second-Generation antipsychotics easy artificial neural network and logistic regression models. J. Clin. Psychiatry 71(3):225–234, 2010.

    Article  CAS  PubMed  Google Scholar 

  19. Hirose, H., Takayama, T., Hozawa, S., Hibi, T., Saito, I., Prediction of metabolic syndrome using artificial neural network system based on clinical data including insulin resistance index and serum adiponectin. Comput. Biol. Med. 41:1051–1056, 2011.

    Article  CAS  PubMed  Google Scholar 

  20. Chen, H., Xiong, S., Xuan Ren, X., Evaluating the Risk of Metabolic Syndrome Based on an Artificial Intelligence Model. Abstr. Appl. Anal. 2014:207268, 12, 2014. doi:10.1155/2014/207268.

    Google Scholar 

  21. Murguía-Romero, M., Jiménez-Flores, R., Méndez-Cruz, A. R., Villalobos-Molina, R.: Predicting metabolic syndrome with neural networks. In: Castro, F., Gelbukh, A., González, A. (Eds.) MICAI 2013, Part I, LNAI 8265, pp. 464–472 (2013)

  22. collab=World Health Organization, Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech. Rep. Ser. 894:1–253, 2000.

    Google Scholar 

  23. Ashwell M., Gunn P., Gibson S., Waist-to height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes. Res., 2011. doi:10.1111/j.1467-789X.2011.00952.x.

  24. Kupusinac, A., Stokić, E., Srdić, B., Determination of WHtR limit for predicting hyperglycemia in obese persons by using artificial neural networks. TEM J. 1:270–272, 2012.

    Google Scholar 

  25. Cybenko, D. L., Approximation by superpositions of a sigmoidal function. Math. Control Signals Syst. 2: 303–314, 1989.

    Article  Google Scholar 

  26. Kupusinac, A., Stokić, E, Lečić, D., Tomić-Naglić, D., Srdić-Galić, B., Gender-, age-, and BMI-specific threshold values of sagittal abdominal diameter obtained by artificial neural networks. Journal of Medical and Biological Engineering 35(6):783–788, 2015.

    Article  Google Scholar 

Download references

Acknowledgments

This work was partially supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia within the projects: ON 174026 and III 044006, and by the Provincial Secretariat for Science and Technological Development of the Autonomous Province of Vojvodina within the project: 114-451-2856/2016.

Author information

Authors and Affiliations

  1. Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, 21000, Novi Sad, Serbia

    Darko Ivanović, Aleksandar Kupusinac, Rade Doroslovački & Dragan Ivetić

  2. Medical Faculty, University of Novi Sad, Hajduk Veljkova 3, 21000, Novi Sad, Serbia

    Edita Stokić

Authors
  1. Darko Ivanović
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aleksandar Kupusinac
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Edita Stokić
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rade Doroslovački
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dragan Ivetić
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aleksandar Kupusinac.

Additional information

This article is part of the Topical Collection on Personal Health Systems for Chronic Diseases Monitoring

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ivanović, D., Kupusinac, A., Stokić, E. et al. ANN Prediction of Metabolic Syndrome: a Complex Puzzle that will be Completed. J Med Syst 40, 264 (2016). https://doi.org/10.1007/s10916-016-0601-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-016-0601-7

Keywords