Skip to main content
SpringerLink
Log in

Rough set–BPSO model for predicting vitamin D deficiency in apparently healthy Kuwaiti women based on hair mineral analysis

  • Review
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Vitamin D deficiency is prevalent in the Arabian Gulf region, especially among women. Recent researches show that, the vitamin D deficiency is associated with mineral status of patient. Therefore, it is important to assess the mineral status of patient to reveal the hidden mineral imbalance associated with vitamin D deficiency. A well-known test such as the red blood cells is fairly expensive, invasive, and less informative. On the other hand, a hair mineral analysis can be considered an accurate, excellent, highly informative tool to measure mineral imbalance associated with vitamin D deficiency. In this study, 120 apparently healthy Kuwaiti women were assessed for their mineral levels and vitamin D status by hair and serum samples, respectively. This information was used to build a computerized model that would predict vitamin D deficiency based on its association with the levels and ratios of minerals. The model introduces a two-stage reduction technique based on BPSO and rough set theory as attribute reduction and rules extraction to predicting vitamin D deficiency. The results show that the proposed model (RS + BPSO), not only can effectively detect the deficiency in vitamin D, but can also provide valuable information with regard to the mineral imbalance as a cause of deficiency which should be addressed in any treatment management. To the best of our knowledge, this is the first work that predicts vitamin D deficiency based on hair minerals analysis.

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

Access this article

Log in via an institution

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
Fig. 4

Similar content being viewed by others

References

  1. Bassil D, Rahme M, Hoteit M, Fuleihan GE-H (2013) Hypovitaminosis D in the Middle East and North Africa. Derm Endocrinol 5(2):274–298

    Article  Google Scholar 

  2. Mishal A (2001) Effects of different dress styles on vitamin D levels in healthy young Jordanian women. Osteoporos Int 12(11):931–935

    Article  Google Scholar 

  3. Siddiqui A, Kamfar H (2007) Prevalence of vitamin D deficiency rickets in adolescent school girls in Western region, Saudi Arabia. Saudi Med J 28(3):441–444

    Google Scholar 

  4. Alyahya K, Lee WTK, Al-Mazidi Z, Morgan J, Lanham-New S (2014) Risk factors of low vitamin D status in adolescent females in Kuwait: implications for high peak bone mass attainment. Arch Osteoporosis 9:1–11

    Article  Google Scholar 

  5. Holick MF (2007) Vitamin D deficiency. N Engl J Med 357(3):266–281

    Article  Google Scholar 

  6. Javaid M, Crozier S, Harvey N, Gale C, Dennison E, Boucher B, Arden N, Godfrey K, Cooper C (2006) Maternal vitamin D status during pregnancy and childhood bone mass at age 9 years: a longitudinal study. Lancet 367(9504):36–43

    Article  Google Scholar 

  7. Autier P, Boniol M, Pizot C, Mullie P (2014) Vitamin D status and ill health: a systematic review. Lancet Diabetes Endocrinol 2:76–89

    Article  Google Scholar 

  8. Kulie T, Groff A, Redmer J, Hounshell J, Schrager S (2009) Vitamin D: an evidence-based review. JABFM 22(6):698–706

    Article  Google Scholar 

  9. Reid IR, Bolland MJ, Grey A (2014) Effects of vitamin D supplements on bone mineral density: a systematic review and meta-analysis. Lancet 383:146–155

    Article  Google Scholar 

  10. Watts D (1990) Nutrient interrelationships minerals–vitamins–endocrines. J Orthomol Med 5(1):11–19

    Google Scholar 

  11. Nguyen T, Khosravi A, Creighton D, Nahavandi S (2015) Classification of healthcare data using genetic fuzzy logic system and wavelets. Expert Syst Appl 42(4):2184–2197

    Article  Google Scholar 

  12. Srimani PK, Koti MS (2011) The impact of rough set approach on medical diagnosis for cost effective feature selection. IJCR 3(12):175–178

    Google Scholar 

  13. Inbarania HH, Azarb AT, Jothi G (2014) Supervised hybrid feature selection based on PSO and rough sets for medical diagnosis. Comput Methods Progr Biomed 113:175–185

    Article  Google Scholar 

  14. Macas M, Bhondekar AP, Kumar R, Kaur R, Kuzilek J, Gerla V, Lhotská L, Kapur P (2014) Binary social impact theory based optimization and its applications in pattern recognition. Neurocomputing 132:85–96

    Article  Google Scholar 

  15. Kumar SU, Inbarani HH (2015) A novel neighborhood rough set based classification approach for medical diagnosis. Proc Comput Sci 47:351–359

    Article  Google Scholar 

  16. Rzepiński T (2014) Randomized controlled trials versus rough set analysis: two competing approaches for evaluating clinical data. Theor Med Bioeth 35(4):271–288

    Article  Google Scholar 

  17. Guo S, Lucas RM, Ponsonby A-L, The Ausimmune Investigator Group (2013) A novel approach for prediction of vitamin D status using support vector regression. PLoS One 8(11):e79970

    Article  Google Scholar 

  18. Wilson L (2010) Nutritional balancing and hair mineral analysis, 4th edn. LD Wilson Consultants, Inc, Prescott

    Google Scholar 

  19. Ahmad G, Kuhi H, Mohit A (2013) A review hair tissue analysis: an analytical method for determining essential elements, toxic elements, hormones and drug use and abuse. Int Res J Appl Basic Sci 4(11):3675–3688

    Google Scholar 

  20. Qayyum MA, Shah MH (2014) Comparative study of trace elements in blood, scalp hair and nails of prostate cancer patients in relation to healthy donors. Biol Trace Elem Res 162:46–57

    Article  Google Scholar 

  21. Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11:341–356

    Article  MATH  Google Scholar 

  22. Talbi E-G, Jourdan L, Garcia-Nieto J, Alba E (2008) Comparison of population based metaheuristics for feature selection: application to microarray data classification. In: Proceedings of AICCSA 2008

  23. Kennedy J, Eberhart RC (1999) The particle swarm: social adaptation in information processing systems. In: Corne D, Dorigo M, Glover F (eds) New ideas in optimization. McGraw-Hill, London

    Google Scholar 

  24. Shi Y, Eberhart RC (1998) Parameter selection in particle swarm optimization. In: Evolutionary programming VII: proceedings of the 7th annual conference on evolutionary programming, New York, pp 591–600

  25. Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. In: Proceedings of the world multi conference on systemics, cybernetics and informatics, Piscataway, pp 4104–4109

  26. Wang X, Yang JXT, Xia W, Jensen R (2007) Feature selection based on rough sets and particle swarm optimization. Pattern Recogn Lett 28:459–471

    Article  Google Scholar 

  27. Inbarani HH, Banu PKN, Andrews S (2012) Unsupervised hybrid PSO-quick reduct approach for feature reduction. In: Proceedings of international conference on recent trends in information technology, pp 11–16

  28. Zuhtuogullari K, Allahverdi N, Arikan N (2013) Genetic algorithm and rough sets based hybrid approach for reduction of the input attributes in medical systems. Int J Innov Comput Inf Control 9(7):3015–3037

    Google Scholar 

  29. Saini S, Rambli DRA, Zakaria N, Sulaiman S (2014) A review on particle swarm optimization algorithm and its variants to human motion tracking. Math Probl Eng. doi:10.1155/2014/704861

    Google Scholar 

  30. Zhong N, Skowron A (2001) Rough sets based knowledge discovery process. Int J Appl Math Comput Sci 11(3):603–619

    MathSciNet  MATH  Google Scholar 

  31. Bazan JG, Szczuka M (2005) The rough set exploration system, transactions on rough sets III. Springer, Berlin, pp 37–56

    Book  MATH  Google Scholar 

  32. Group of logic in poland. RSES. http://logic.mimuw.edu.pl/~rses

  33. Own HS, Hassanien AE (2009) Rough sets: a medical knowledge discovery (RS-MKD) scheme for prostate cancer data analysis. Int J Comput Sci Softw Technol (IJCSST) 2(2):999–1005

    Google Scholar 

  34. http://iom.nationalacademies.org/

  35. Lamberg-Allardt C, Brustad M, Meyer H, Steingrimsdottir L (2013) Vitamin D—a systematic literature review for the 5th edition of the nordic nutrition recommendations. Food Nutr Res 5:57

    Google Scholar 

  36. Wang Q (2014) A hybrid sampling SVM approach to imbalanced data classification, abstract and applied analysis. Hindawi Publishing Corporation, Cairo. doi:10.1155/2014/972786 (SD-008)

    Google Scholar 

  37. Own HS, Abraham A (2012) A new weighted rough set framework based classification for egyptian neonatal jaundice. Appl Soft Comput Elsevier 12(3):999–1005

    Article  Google Scholar 

  38. Schwalfenberg GK, Genuis SJ (2015) Vitamin D, essential minerals, and toxic elements: exploring interactions between nutrients and toxicants in clinical medicine. Sci World J. doi:10.1155/2015/318595

  39. Kebapcilar A, Kulaksizoglu M, Kebapcilar L, Gonen M, Ünlü A, Topcu A, Demirci F, Taner C (2013) Is there a link between premature ovarian failure and serum concentrations of vitamin D, zinc, and copper? Menopause 20(1):94–99

    Article  Google Scholar 

  40. Villagomez A, Ramtekkar U (2014) Iron, magnesium, vitamin D, and zinc deficiencies in children presenting with symptoms of attention-deficit/hyperactivity disorder. Children 1(3):261 (1994)

    Article  Google Scholar 

  41. Moon J (1994) The role of vitamin D in toxic metal absorption: a review. J Am Coll Nutr 13(6):559–564

    Article  Google Scholar 

  42. Rodriguez-Martinez MA, Garcia-Cohen EC (2002) Role of Ca(2+) and vitamin D in the prevention and treatment of osteoporosis. Pharmacol Theraputics 93(1):37–49

    Article  Google Scholar 

  43. Zittermann A (2013) Magnesium deficit—overlooked cause of low vitamin D status? BMC Med 11:229

    Article  Google Scholar 

  44. Deng X, Song Y, Manson JE, Signorello LB, Zhang SM, Shrubsole MJ, Ness RM, Seidner DL, Dai Q (2013) Magnesium, vitamin D status and mortality: results from US National Health and Nutrition Examination Survey (NHANES) 2001 to 2006 and NHANES III. BMC Med 11(187):1741–7015

    Google Scholar 

  45. Worker NA, Migicovsky BB (1961) Effect of vitamin D on the utilization of zinc, cadmium and mercury in the chick. J Nutr 75:222–224

    Google Scholar 

  46. Kechrid Z, Hamdi M, Naziroglu M, Flores-Arce M (2012) Vitamin D supplementation modulates blood and tissue zinc, liver glutathione and blood biochemical parameters in diabetic rats on a zinc-deficient diet. Biol Trace Elem Res 148(3):371–377

    Article  Google Scholar 

  47. Masuhara T, Migicovsky BB (1963) Vitamin D and the intestinal absorption of iron and cobalt. J Nutr 80:332–336

    Google Scholar 

  48. Terry J (1994) The major electrolytes: sodium, potassium, and chloride. J Intraven Nurs 17(5):240–247

    Google Scholar 

  49. Archibeque-Engle SL, Tessari JD, Winn DT, Keefe TJ, Nett TM, Zheng T (1997) Comparison of organochlorine pesticide and polychlorinated biphenyl residues in human breast adipose tissue and serum. J Toxicol Environ Health 52:285–293

    Article  Google Scholar 

  50. Ross AC (2011) The 2011 report on dietary reference intakes for calcium and vitamin D. Public Health Nutr 14(5):938–939

    Article  Google Scholar 

Download references

Acknowledgments

The authors are gratefully thanks the Public Authority for Applied Education and Training (PAAET), Kuwait for providing the dataset used in this study.

Author information

Authors and Affiliations

  1. Department of Solar and Space Research, National Research Institute of Astronomy and Geophysics, El-Marsad Street, P. O. Box 11421, Helwan, Egypt

    Hala S. Own

  2. Science Department, College of Basic Education, Public Authority for Applied Education and Training, Kuwait, Kuwait

    Khulood O. Alyahya

  3. Department of Computer Information Systems, College of Business Studies, Public Authority of Applied Education and Training, Kuwait, Kuwait

    Waheeda I. Almayyan

  4. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Auburn, WA, USA

    Ajith Abraham

Authors
  1. Hala S. Own
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Khulood O. Alyahya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Waheeda I. Almayyan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ajith Abraham
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hala S. Own.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Own, H.S., Alyahya, K.O., Almayyan, W.I. et al. Rough set–BPSO model for predicting vitamin D deficiency in apparently healthy Kuwaiti women based on hair mineral analysis. Neural Comput & Applic 29, 329–344 (2018). https://doi.org/10.1007/s00521-016-2454-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-016-2454-x

Keywords

Search

Navigation