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Artificial Intelligence Models for Predicting Iron Deficiency Anemia and Iron Serum Level Based on Accessible Laboratory Data

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Abstract

Iron deficiency anemia (IDA) is the most common nutritional deficiency worldwide. Measuring serum iron is time consuming, expensive and not available in most hospitals. In this study, based on four accessible laboratory data (MCV, MCH, MCHC, Hb/RBC), we developed an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) to diagnose the IDA and to predict serum iron level. Our results represent that the neural network analysis is superior to ANFIS and logistic regression models in diagnosing IDA. Moreover, the results show that the ANN is likely to provide an accurate test for predicting serum iron levels with high accuracy and acceptable precision.

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References

  1. Grosbois, B., Decaux, O., Cador, B., Cazalets, C., and Jego, P., Human iron deficiency. Bull. Acad. Natl Méd. 189:1649–1663, 2005.

    Google Scholar 

  2. Haas, J. D., and Brownlie, T., IV, Iron deficiency and reduced work capacity: a critical review of the research to determine a causal relationship. J. Nutr. 131(2 suppl):676S–88S, 2001. discussion 688S–90S.

    Google Scholar 

  3. Halterman, J. S., Kaczorowski, J. M., Aligne, C. A., Auinger, P., and Szilagyi, P. G., Iron deficiency and cognitive achievement among school-aged children and adolescents in the United States. Pediatrics 107:1381–6, 2001.

    Article  Google Scholar 

  4. Cook, J. D., and Skikne, B. S., Iron deficiency: definition and diagnosis. J. Intern. Med. 226(5):349–55, 1989.

    Article  Google Scholar 

  5. Worwood, M., The laboratory assessment of iron status: an update. Clin. Chim. Acta 259:3–23, 1997.

    Article  Google Scholar 

  6. Cross, S. S., Harrison, R. F., and Kennedy, R. L., Introduction to neural networks. Lancet 346:1075–1079, 1995.

    Article  Google Scholar 

  7. Dariani, S., Keshavarz, M., Parviz, M., Raoufy, M. R., and Gharibzadeh, S., Modeling force-velocity relation in skeletal muscle isotonic contraction using an artificial neural network. Biosystems 90(2):529–34, 2007.

    Article  Google Scholar 

  8. Forsstrom, J. J., and Dalton, K. J., Artificial neural networks for decision support in clinical medicine. Ann. Med. 27:509–17, 1995.

    Article  Google Scholar 

  9. Rodvold, D. M., McLeod, D. G., Brandt, J. M., Snow, P. B., and Murphy, G. P., Introduction to artificial neural networks for physicians: taking the lid off the black box. Prostate 46:39–44, 2001.

    Article  Google Scholar 

  10. Boutsinas, B., and Vrahatis, M., Artificial nonmonotonic neural networks. Artif. Intell. 132:1–38, 2001.

    Article  MathSciNet  MATH  Google Scholar 

  11. Lisboa, P., A review of evidence of health benefit from artificial neural networks in medical intervention. Neural Netw. 15:11–39, 2002.

    Article  Google Scholar 

  12. Ramesh, A. N., Kambhampati, C., Monson, J. R., and Drew, P. J., Artificial intelligence in medicine. Ann. R. Coll. Surg. Engl. 86:334–8, 2004.

    Article  Google Scholar 

  13. Sharpe, P. K., and Caleb, P., Artificial neural networks within medical decision support systems. Scand. J. Clin. Lab. Invest. Suppl. 219:3–11, 1994.

    Article  Google Scholar 

  14. Tafeit, E., and Reibnegger, G., Artificial neural networks in laboratory medicine and medical outcome prediction. Clin. Chem. Lab. Med. 37:845–53, 1999.

    Article  Google Scholar 

  15. Winkler, D. A., Neural networks as robust tools in drug lead discovery and development. Mol. Biotechnol. 27:139–68, 2004.

    Article  Google Scholar 

  16. Dytch, H. E., and Wied, G. L., Artificial neural networks and their use in quantitative pathology. Anal. Quant. Cytol. Histol. 12:379–93, 1990.

    Google Scholar 

  17. Tu, J. V., Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J. Clin. Epidemiol. 49:1225–1231, 1996.

    Article  Google Scholar 

  18. Eftekhar, B., Mohammad, K., Ardebili, H. E., Ghodsi, M., and Ketabchi, E., Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data. BMC Med. Inform. Decis. Mak. 5:3, 2005.

    Article  Google Scholar 

  19. Raoufy, M. R., Vahdani, P., Alavian, S. M., Fekri, S., Eftekhari, P., and Gharibzadeh, S., A novel method for diagnosing cirrhosis in patients with chronic hepatitis B: artificial neural network approach. J. Med. Syst. 35(1):121–6, 2011. Epub 2009 Jul 21.

    Article  Google Scholar 

  20. Hanley, J. A., and McNeil, B. J., The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1):29–36, 1982.

    Google Scholar 

  21. Swets, J. A., Measuring the accuracy of diagnostic systems. Science 240:1285–1293, 1988.

    Article  MathSciNet  MATH  Google Scholar 

  22. Lin, C. S., Li, Y. C., Mok, M. S., Wu, C. C., Chiu, H. W., and Lin, Y. H., Neural network modeling to predict the hypnotic effect of propofol bolus induction. Proc. AMIA Symp. 450–454, 2002.

  23. Ghoshal, U. C., and Das, A., Models for prediction of mortality from cirrhosis with special reference to artificial neural network: a critical review. Hepatol. Int. 2(1):31–8, 2008. Epub 2007.

    Article  Google Scholar 

  24. Chong, C. F., Li, Y. C., Wang, T. L., Chang, H., Stratification of adverse outcomes by preoperative risk factors in coronary artery bypass graft patients: an artificial neural network prediction model. AMIA Annu. Symp. Proc. 160–164, 2003.

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Authors and Affiliations

  1. Amirkabir University of Technology, Tehran, Iran

    Iman Azarkhish, Mohammad Reza Raoufy & Shahriar Gharibzadeh

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  1. Iman Azarkhish
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  2. Mohammad Reza Raoufy
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  3. Shahriar Gharibzadeh
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Correspondence to Shahriar Gharibzadeh.

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Azarkhish, I., Raoufy, M.R. & Gharibzadeh, S. Artificial Intelligence Models for Predicting Iron Deficiency Anemia and Iron Serum Level Based on Accessible Laboratory Data. J Med Syst 36, 2057–2061 (2012). https://doi.org/10.1007/s10916-011-9668-3

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  • DOI: https://doi.org/10.1007/s10916-011-9668-3

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