Advertisement

Correlation between drinking water fluoride and TSH hormone by ANNs and ANFIS

  • Zohreh Kheradpisheh
  • Amir Hossein Mahvi
  • Masoud Mirzaei
  • Mehdi Mokhtari
  • Reyhane Azizi
  • Hossein Fallahzadeh
  • Mohammad Hassan Ehrampoush
Research Article
  • 74 Downloads

Abstract

Background

Artificial neural networks (ANNs) and adaptive neural-fuzzy Inference system (ANFIS) are the best solutions to finding the correlation between some water parameters and human hormones. The correlation between thyroid stimulating hormone (TSH) and drinking water fluoride studied by ANNS and ANFIS models in Yazd city.

Method

In this study, eighty people with thyroid gland disorder and 213 healthy people invited. Their thyroid hormones and fluoride drinking water analyzed.

Results

The result of ANFIS showed R2 = 0.81 for test and R2 = 0.85 for train in all cases and controls data. This results were R2 = 0.73 and R2 = 0.81 for ANNs respectively.

Conclusion

This models can be used as an alternative for show correlation between Drinking Water Fluoride and TSH Hormone and R2 = 0.85 gained from ANFIS was the best.

Keywords

Fluoride Drinking water Thyroid stimulating hormone (TSH) Artificial neural networks (ANNS) Adaptive neural-fuzzy inference system (ANFIS) 

Abbreviations

ANFIS

Adaptive neural-fuzzy inference system

ANNS

Artificial neural networks

NRMSE

Normalized root-mean-square deviation

R2

R-squared

RMSE

root-mean-squared error

T3

Triiodothyronine

T4

Thyroxin

Train GD

Gradient descent

Train GDA

Gradient descent with adaptive learning rate back propagation

Train GDX

Variable Learning Rate Back propagation

Train LM

Levenberg-Marquardt

Train SCG

Scaled Conjugate Gradient

TSH

Thyroid stimulating hormone

YaHS

Yazd Healthy Study

Notes

Acknowledgements

We acknowledge Yazd Healthy Study participants who gave us their time and drinking water samples.

Author contributions

ZKH and MHE was the main investigator, collected the data, MM, MM and AHM supervised the study. MK and RA and HF were advisors of the study. All authors read and approved the final manuscript.

Funding

This research was supported by Yazd University of Medical Sciences.

Compliance with ethical standards

This study is Compliance with Ethical Standards. This study funded by Environmental Science and Technology Research Center, Department of Environmental Health Engineering, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.

Ethical approval

Case and control people choses with their consent and have been informed about study and they invited for Thyroid hormones test. Their thyroid hormones analyze in Yazd Central Laboratory.

Competing interests

The authors declare that they have no competing interests.

References

  1. 1.
    Basha PM, Rai P, Begum S. Fluoride toxicity and status of serum thyroid hormones, brain histopathology, and learning memory in rats: a multigenerational assessment. Biol Trace Elem Res. 2011;144:1083–94.CrossRefGoogle Scholar
  2. 2.
    Dey S, Giri B. Fluoride fact on human health and health problems: a review. Med Clin Rev. 2016;2(1):11.  https://doi.org/10.21767/2471-299X.100011.CrossRefGoogle Scholar
  3. 3.
    KheradPisheh Z, Ehrampoush MH, Montazeri A, Mirzaei M, Mokhtari M, Mahvi AH. Fluoride in drinking water in 31 provinces of Iran. Expo Health. 2016;8:465–74.  https://doi.org/10.1007/s12403-016-0204-z.CrossRefGoogle Scholar
  4. 4.
    Lennon M A, Whelton H, O’Mullane D, Ekstrand J. Fluoride. Rolling revision of the WHO guidelines for drinking-water quality. World Health Organization. 2004.Google Scholar
  5. 5.
    Peckham S, Awofeso N. Water fluoridation: a critical review of the physiological effects of ingested fluoride as a public health intervention. Sci World J. 2014;2014:293019.CrossRefGoogle Scholar
  6. 6.
    Peckham S, Lowery D, Spencer S. Are fluoride levels in drinking water associated with hypothyroidism prevalence in England? A large observational study of GP practice data and fluoride levels in drinking water. J Epidemiol Community Health. 2015;0:1–6.  https://doi.org/10.1136/jech-2014-204971. Google Scholar
  7. 7.
    Rahmani A, Rahmani K, Dobaradaran S, Mahvi AH. Hamadjani RM, Rahmani H. Child dental caries in relation to fluoride and some inorganic constituents in drinking water in Arsanjan, Iran. Fluoride. 2010;43:179–86.Google Scholar
  8. 8.
    Dobaradaran S, Mahvi AH, Dehdashti S, Abadi DRV. Drinking water fluoride and child dental caries in Dashtestan, Iran. Fluoride. 2008;41:220–6.Google Scholar
  9. 9.
    Mahvi AH, Zazoli MA, Younecian M, Nicpour B, Babapour A. Survey of fluoride concentration in drinking water sources and prevalence of DMFT in the 12 years old students in Behshar City. J Med Sci. 2006;6:658–61.CrossRefGoogle Scholar
  10. 10.
    Rahmani A, Rahmani K, Mahvi AH, Usefie M. Drinking water fluoride and child dental caries in Noorabademamasani, Iran. Fluoride. 2010;43:187–93.Google Scholar
  11. 11.
    Dobaradaran S, Mahvi AH, Dehdashti S. Fluoride content of bottled drinking water available in Iran. Fluoride. 2008;41:93–4.Google Scholar
  12. 12.
    Dobaradaran S, Mahvi AH, Dehdashti S, Dobaradaran S, Shoara R. Correlation of fluoride with some inorganic constituents in groundwater of Dashtestan, Iran. Fluoride. 2008;42:50–3.Google Scholar
  13. 13.
    Mahvi AH, Zazoli MA, Younecian M, Esfandiari Y. Fluoride content of Iranian black tea and tea liquor. Fluoride. 2006;39:266–8.Google Scholar
  14. 14.
    Nouri J, Mahvi AH, Babaei A, Ahmadpour E. Regional pattern distribution of groundwater fluoride in the shush aquifer of Khuzestan County, Iran. Fluoride. 2006;39:321–5.Google Scholar
  15. 15.
    Zazouli MA, Mahvi AH, Dobaradaran S, Barafrashtehpour M, Mahdavi Y, Balarak D. Adsorption of fluoride from aqueous solution by modified Azolla filiculoides. Fluoride. 2014;47:349–58.Google Scholar
  16. 16.
    Bazrafshan E, Ownagh KA, Mahvi AH. Application of electrocoagulation process using Iron and aluminum electrodes for fluoride removal from aqueous environment. E-J Chem. 2012;9:2297–308.CrossRefGoogle Scholar
  17. 17.
    Boldaji MR, Mahvi AH, Dobaradaran S, Hosseini SS. Evaluating the effectiveness of a hybrid sorbent resin in removing fluoride from water. Int J Environ Sci Technol. 2009;6:629–32.CrossRefGoogle Scholar
  18. 18.
    Dobaradaran S, Fazelinia F, Mahvi AH, Hosseini SS. Particulate airborne fluoride from an aluminium production plant in arak, Iran. Fluoride. 2009;42:228–32.Google Scholar
  19. 19.
    World Health Organization. Guidelines for Drinking-water Quality; 2011. Fourth edition, ISBN 978 92 4 154815 1.Google Scholar
  20. 20.
    Rastogi BA, Monika A. Study of neural network in diagnosis of thyroid. IJCTEE. 2014;4(3):13–6.Google Scholar
  21. 21.
    Caturegli P, Remigis A, Rose NR. Hashimoto thyroiditis: clinical and diagnostic criteria. Autoimmun Rev. 2014;13:391–7.CrossRefGoogle Scholar
  22. 22.
    Emin Aktan M, Akdogan E, Zengin N, Guney OF, Parlar RE. An artifitial neural network design for determination of Hashimoto’s thyroiditis sub- groups, CBU international on innovations in science and education. 2016:23-25, Prague, Czech Repablic WWW.CBUNI.CZ, WWW.JOURNALS.CZ
  23. 23.
    Health Information. Endocrine Diseases. Hashimoto's Disease. 2016. www.niddk.nih.gov/health-information/health-topics/endocrine/hashimotos-disease/16.03.2016.
  24. 24.
    Omitek Z, Burda A, Wojcik W. The use of decision tree induction and artificial neural networks for automatic diagnosis of Hashimoto’s disease. Expert Syst Appl. 2013;40:6684–9.CrossRefGoogle Scholar
  25. 25.
    Sundaram N M, Renupriya V. Artificial neural network classifiers for diagnosis of thyroid abnormalities. International conference on systems, science, control, communication, Eng Technol 2016: 206–211.Google Scholar
  26. 26.
    Zhang GP, Berardi VL. An investigation of neural networks in thyroid function diagnosis. Health Care Manage Sci. 1998;1:29–37.CrossRefGoogle Scholar
  27. 27.
    Ozyılmaz L, Yıldırım T. Diagnosis of thyroid disease using artificial neural network methods. In: Proceedings of ICONIP’02 9th international conference on neural information processing (Singapore: Orchid Country Club, 2002). 2002, p. 2033–2036.Google Scholar
  28. 28.
    Soleimanian Gharehchopogh F, Molany M, Dabaghchi MF. Using artificial neural network in diagnosis of thyroid disease: a case study. IJCSA. 2013;3(4):49–61.  https://doi.org/10.5121/ijcsa.2013.3405.Google Scholar
  29. 29.
    Innocent PR, John RI, Garibaldi JM. Fuzzy methods for medical diagnosis. Appl Artif Intell. 2004;19(1):69–98.CrossRefGoogle Scholar
  30. 30.
    Zarandi F, Zolnoori MH, Moin M, Heidarnejad HA. Fuzzy rule-based expert system for diagnosing asthma. Sharif University of Technology. Transaction E. Ind Eng. 2010;17(2):129–42. www.SID.ir Google Scholar
  31. 31.
    Ghahazi MA, Zarandi F, Harirchian MH, Damirchi-Darasi SR. Fuzzy rule based expert system for diagnosis of multiple sclerosis. IEEE conference on Norbert wiener in the 21st century (21CW), Boston, MA. 2014: 1–5.Google Scholar
  32. 32.
    Amrollahi Biyouki S, Türksen IB, Fazel Zarandi MH. Fuzzy rule-based expert system for diagnosis of thyroid disease. IEEE. International Conference on Fuzzy Systems. Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). 2015 I.E. Conference. Niagara Falls, ON, Canada 19 October 2015.Google Scholar
  33. 33.
    Saylam B, Keskek M, Ocak S, Akten AO, Tez M. Artificial neural network analysis for evaluating cancer risk in multinodular goiter. J Res Med Sci. 2013;18(7):554–7.Google Scholar
  34. 34.
    Er O, Temurtas F, Tanrıkulu AÇ. Tuberculosis disease diagnosis using artificial neural networks. J Med Syst 2013. 2010;34(3):299–302.  https://doi.org/10.1007/s10916-008-9241-x.Google Scholar
  35. 35.
    Castanho MJP, Hernandes F, De Re AM, Rautenberg S, Billis A. Fuzzy expert system for predicting pathological stage of prostate cancer. Expert Syst Appl. 2013;40:466–70.CrossRefGoogle Scholar
  36. 36.
    Takahashi M, Hayashi H, Watanabe Y. Diagnostic classification of schizophrenia by neural network analysis of blood-based gene expression signatures. Schizophr Res. 2010;119:210–8.CrossRefGoogle Scholar
  37. 37.
    Kaya E, Aktan ME, Akdoğan E, Koru AT. Diagnosis of anemia in children via artificial neural network. IJISAE. 2015;3(1):24–7. www.atscience.org/IJISAE CrossRefGoogle Scholar
  38. 38.
    Ebenezer O, Oyebade O, Oyedotun K, Helwan A. Neural network diagnosis of heart disease, Conference Paper 2015.  https://doi.org/10.1109/ICABME.2015.7323241.
  39. 39.
    Lahner E, Intraligi M, Buscema M, CentANNsi M, VANNsella L, Grossi E, et al. Artificial neural networks in the recognition of the presence of thyroid disease in patients with atrophic body gastritis. World J Gastroenterol. 2008;14(4):563–8.CrossRefGoogle Scholar
  40. 40.
    Census of the Islamic Republic of Iran. Islamic Republic of Iran. Archived from the original (excel) on. 2011–11-11. 2006.Google Scholar
  41. 41.
    Lenore S, Arnold E, Andrew D. Standard methods for the examination of water and wastewater, American public health association, American Water Works Association, water environment federation. The twentieth edition. 4500-F– D. SPADNS Method. 2005;20:140–3.Google Scholar
  42. 42.
    Boyacioglu MA, Avci D. An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: the case of the Istanbul stock exchange. Expert Syst Appl. 2010;37:7908–12.CrossRefGoogle Scholar
  43. 43.
    Rezaei Kahkha MR, Piri J. Comparison of artificial neural network and neutral-fuzzy inference system for photo catalytic removal of reactive red dye. Tech J Engine App Sci. 2016;6(1):39–44.Google Scholar
  44. 44.
    Razia SH, Narasinga Rao MR. Machine learning techniques for thyroid disease diagnosis - a review. Indian J Sci Technol. 2016;9(28)  https://doi.org/10.17485/ijst/2016/v9i28/93705, July.
  45. 45.
    Svalina L, Galzina V, Lujic R, Šimunovic G. An adaptive network-based fuzzy inference system (ANFIS) for the forecasting: the case of close price indices. Expert Syst Appl. 2013;40:6055–63.CrossRefGoogle Scholar
  46. 46.
    Adeli A, Neshat M. A Fuzzy Expert System for Heart Disease Diagnosis in Proceedings of the International Multi Conference of Engineers and Computer Scientists, Hong Kong. March 17–19 2010; 1Hong Kong.Google Scholar
  47. 47.
    Galletti P, Joyet G. Effect of fluorine on thyroidal iodine metabolism in hyperthyroidism. J Clin Endocrinol. 1958;18(10):1102–10.CrossRefGoogle Scholar
  48. 48.
    Prerana SP, Taneja K. Predictive Data Mining for Diagnosis of Thyroid Disease using Neural Network. Int J Res Manage Sci Technol. 2015;3(2):75–80. Available at www.ijrmst.org Google Scholar
  49. 49.
    Mirzaei, M., Salehi-Abargouei, A., Mirzaei, M., Mohsenpour, M.A. Cohort profile: the Yazd health study (YaHS): a population-based study of adults aged 20–70 years (study design and baseline population data). Int J Epidemiol 1–10 (2017).  https://doi.org/10.1093/ije/dyx231.

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Zohreh Kheradpisheh
    • 1
  • Amir Hossein Mahvi
    • 2
    • 3
  • Masoud Mirzaei
    • 4
  • Mehdi Mokhtari
    • 1
  • Reyhane Azizi
    • 5
  • Hossein Fallahzadeh
    • 6
  • Mohammad Hassan Ehrampoush
    • 1
  1. 1.Environmental Science and Technology Research Center, Department of Environmental Health EngineeringShahid Sadoughi University of Medical SciencesYazdIran
  2. 2.School of Public HealthTehran University of Medical SciencesTehranIran
  3. 3.Center for Solid Waste Research, Institute for Environmental ResearchTehran University of Medical SciencesTehranIran
  4. 4.Yazd Cardiovascular Research CenterShahid Sadoughi University of Medical SciencesYazdIran
  5. 5.Department of EndocrinologyShahid Sadoughi University of Medical SciencesYazdIran
  6. 6.Department of BiostatisticsShahid Sadoughi University of Medical SciencesYazdIran

Personalised recommendations