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An Application of Artificial Immune Recognition System for Prediction of Diabetes Following Gestational Diabetes

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Abstract

Diabetes mellitus (DM) is a disease prevalent in population and is not easily perceived in its initial stage but may sway a patient very seriously in later stage. In accordance with the estimation of World Health Organization (WHO), there will be 370 million diabetics which are 5.4% of the global people in 2030, so it becomes more and more important to predict whether a pregnant woman has or is likely to acquire diabetes. This study is conducted with the use of the machine learning—Artificial Immune Recognition System (AIRS)—to assist doctors in predicting pregnant women who have premonition of type 2 diabetes. AIRS is proposed by Andrew Watkins in 2001 and it makes use of the metaphor of the vertebrate immune system to recognize antigens, select clone, and memorize cells. Additionally, AIRS includes a mechanism, limited resource, to restrain the number of memory cells from increasing uncontrollably. It has also showed positive results on problems in which it was applied. The objective of this study is to investigate the feasibility in using AIRS to predict gestational diabetes mellitus (GDM) subsequent DM. The dataset of diabetes has imbalanced data, but the overall classification recall could still reach 62.8%, which is better than the traditional method, logistic regression, and the technique which is thought as one of the powerful classification approaches, support vector machines (SVM).

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References

  1. Madhavan, A., Beena, K. R., and Sanal, M. G., A pilot study on the usefulness of body mass index and waist hip ratio as a predictive tool for gestational diabetes in Asian Indians. Gynecol. Endocrinol. 24:701–707, 2008.

    Article  Google Scholar 

  2. Seshiah, V., Balaji, V., Balaji, M. S., Panneerselvam, A., Thamizharasi, M., and Arthi, T., Glycemic level at the first visit and prediction of GDM. J. Assoc. Phys. India. 55:630–632, 2007.

    Google Scholar 

  3. Akinci, B., Celtik, A., Yener, S., and Yesil, S., Is fasting glucose level during oral glucose tolerance test an indicator of the insulin need in gestational diabetes? Diabetes Res. Clin. Pract. 82(2):219–225, 2008.

    Article  Google Scholar 

  4. Campos, M. A. A., Reichelt, A. A. J., Façanha, C., Forti, A. C., and Schmidt, M. I., Evaluation of a 1-h 75-g oral glucose tolerance test in the diagnosis of gestational diabetes. Braz. J. Med. Biol. Res. 41(8):684–688, 2008.

    Google Scholar 

  5. Cheung, N. W., and Helmink, D., Gestational diabetes: the significance of persistent fasting hyperglycemia for the subsequent development of diabetes mellitus. J. Diabetes Its Complicat. 20(1):21–25, 2006.

    Article  Google Scholar 

  6. Lee, A. J., Hiscock, R. J., Wein, P., Walker, S. P., and Permezel, M., Gestational diabetes mellitus: clinical predictors and long-term risk of developing type 2 diabetes: a retrospective cohort study using survival analysis. Diabetes Care. 30(4):878–883, 2007.

    Article  Google Scholar 

  7. Cho, N. H., Lim, S., Jang, H. C., Park, H. K., and Metzger, B. E., Elevated homocysteine as a risk factor for the development of diabetes in women with a previous history of gestational diabetes mellitus: a 4-year prospective study. Diabetes Care. 28(11):2750–2755, 2005.

    Article  Google Scholar 

  8. Carr, D. B., Newton, K. M., Utzschneider, K. M., Tong, J., Gerchman, F., Kahn, S. E., and Heckbert, S. R., Modestly elevated glucose levels during pregnancy are associated with a higher risk of future diabetes among women without gestational diabetes mellitus. Diabetes Care. 31(5):1037–1039, 2008.

    Article  Google Scholar 

  9. Smirnakis, K. V., Chasan-Taber, L., Wolf, M., Markenson, G., Ecker, J. L., and Thadhani, R., Postpartum diabetes screening in women with a history of gestational diabetes. Obstet. Gynecol. 106(6):1297–1303, 2005.

    Article  Google Scholar 

  10. Watkins, A. B. AIRS: A resource limited artificial immune classifier. Master thesis, Mississippi State University, 2001.

  11. Polat, K., Güneş, S., and Tosun, S., Diagnosis of heart disease using artificial immune recognition system and fuzzy weighted pre-processing. Pattern Recogn. 39(11):2186–2193, 2006.

    Article  Google Scholar 

  12. Polat, K., Yosunkaya, Ş., and Güneş, S., Comparison of different classifier algorithms on the automated detection of obstructive sleep apnea syndrome. J. Med. Syst. 32(3):243–250, 2008.

    Article  Google Scholar 

  13. Polat, K., Yosunkaya, Ş., and Güneş, S., A new approach to diagnosing of importance degree of obstructive sleep apnea syndrome: pairwise AIRS and fuzzy-AIRS classifiers. J. Med. Syst. 32(6):489–497, 2008.

    Article  Google Scholar 

  14. Polat, K., and Güneş, S., Principles component analysis, fuzzy weighting pre-processing and artificial immune recognition system based diagnostic system for diagnosis of lung cancer. Expert Syst. Appl. 34(1):214–221, 2008.

    Article  Google Scholar 

  15. Nasaroui, O. F., Gonzalez, F., and Dasgupta, D., The fuzzy artificial immune system: motivation, basic concepts, and application to clustering and web profiling. International Joint Conference on Fuzzy Systems, pp. 711–717, 2002.

  16. Şahan, S., Kodaz, H., Güneş, S., and Polat, K., A new classifier based on attribute weighted artificial immune system. Lect. Notes Comput. Sci. 3280:11–20, 2004.

    Article  Google Scholar 

  17. Goodman, D. E. Jr., Boggess, L. C., and Watkins, A. B., Artificial immune system classification of multiple-class problems. The Proceeding of the Artificial Neural Networks in Engineering, pp. 179–183, 2002.

  18. Celebi, M. E., Kingravi, H. A., Uddin, B., Iyatomi, H., Aslandogan, Y. A., Stoecker, W. V., and Moss, R. H., A methodological approach to the classification of dermoscopy images. Comput. Med. Imaging Graph. 31(6):362–373, 2007.

    Article  Google Scholar 

  19. Wu, G., and Chang, E. Y., KBA: kernel boundary alignment considering imbalanced data distribution. IEEE Trans. Knowl. Data Eng. 17(6):786–794, 2005.

    Article  Google Scholar 

  20. Batuwita, R., and Palade, V., MicroPred: effective classification on pre-miRNAs for human miRNA gene prediction. Bioinformatics. 25(8):989–995, 2009.

    Article  Google Scholar 

  21. Tang, Y., and Chawla, N. V., SVMs modeling for highly imbalanced classification. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 39(1):281–288, 2009.

    Article  Google Scholar 

  22. Kubat, M., and Matwin, S., Addressing the curse of imbalanced training set: one-sided selection. Proceeding 14th International Conference on Machine Learning (ICML’97) 179–186, 1997.

  23. Chang, C. C., Lin, C. J. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/∼cjlin/libsvm

  24. Hsu, C. W., and Lin, C. J., A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Netw. 12(2):415–425, 2002.

    Google Scholar 

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Acknowledgement

This work was supported in part by the National Science Council, Taiwan, under grant NSC-98-2221-E-007-071-MY3.

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Authors and Affiliations

  1. Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan

    Hung-Chun Lin & Chao-Ton Su

  2. Department of Otolaryngology, Cathay General Hospital, Fu Jen Catholic University School of Medicine, Taipei, Taiwan

    Pa-Chun Wang

  3. Room 820, Engineering Building I, 101, Sec. 2, Kuang Fu Road, Hsinchu, Taiwan, 30013, Republic of China

    Chao-Ton Su

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  1. Hung-Chun Lin
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Correspondence to Chao-Ton Su.

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Lin, HC., Su, CT. & Wang, PC. An Application of Artificial Immune Recognition System for Prediction of Diabetes Following Gestational Diabetes. J Med Syst 35, 283–289 (2011). https://doi.org/10.1007/s10916-009-9364-8

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