Share This Article:

Helicobacter pylori microbe and detecting with data mining algorithms

DOI: 10.4236/ojgas.2013.32016    3,286 Downloads   4,689 Views   Citations

ABSTRACT

Nowadays medicines believe that the only definite method to diagnose the existence of Helicobacter pylori microbe is performing endoscope, however it’s painful and insufferable for young children. Thus in this paper we used data mining algorithms to diagnose the existence of this microbe and eventually we succeeded in predicting the existence of this bacterium in stomach that guides medicines to perform Endoscopy just in cases where percentage of finding this bacterium is high.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Rasekh, A. , Liaghat, Z. and Tabebordbar, A. (2013) Helicobacter pylori microbe and detecting with data mining algorithms. Open Journal of Gastroenterology, 3, 93-98. doi: 10.4236/ojgas.2013.32016.

References

[1] Gold, B., Colletti, R., Abbott, M., et al. (2000) Helicobacter pylori infection in children: Recommendations for diagnosis and treatment. Journal of Pediatric Gastroenterology and Nutrition, 31, 490-497. doi:10.1097/00005176-200011000-00007
[2] Guarner, J., Kalach, N., Elitsur, Y. and Koletzko, S. (2010) Helicobacter pylori diagnostic tests in children: Review of the literature from 1999 to 2009. European Journal of Pediatrics, 169, 15-25. doi:10.1007/s00431-009-1033-x
[3] Begue, R.E., Mirza, A., Compton, T., Gomez, R. and Vargas, A. (1999) Heli-cobacter pylori infection and insulin requirement among children with type 1 diabetes mellitus. Pediatrics, 103, e83. doi:10.1542/peds.103.6.e83
[4] Richter, T., Richter, T., List, S., Müller, D.M., Deutscher, J., Uhlig, H.H., et al. (2001) Five to 7-year-old children with Helicobacter pylori infection are smaller than heliconbacter-negative children: A cross-sectional population-based study of 3315 children. Journal of Pediatric Gastroenterology and Nutrition, 33, 472-475. doi:10.1097/00005176-200110000-00010
[5] Bourke, B., Ceponis, P., Chiba, N., et al. (2005) Canadian helicobacter study group consensus conference: Update on the approach to Helicobacter pylori infection in children and adolescents—An evidence-based evaluation. Canadian Journal of Gastroenterology, 19, 399-408.
[6] Marshall, B. and Warren, J.R. (1984) Unidentified curved bacilli in the stomach of patients with gastritis and peptic ulceration. Lancet, 1, 1311-1314. doi:10.1016/S0140-6736(84)91816-6
[7] Czinn, S. (2005) Helicobacter pylori infection: Detection, investigation and management. Journal of Pediatrics, 146, S21-S26. doi:10.1016/j.jpeds.2004.11.037
[8] Troyanskaya, O.G., Dolinski, K., Owen, A.B., Altman, R.B. and Botstein, A. (2003) Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae). Proceedings of the National Academy of Sciences of the United States of America, 100, 8348-8353. doi:10.1073/pnas.0832373100
[9] Friedman, N., Geiger, D. and Goldszmidt, M. (1997) Bayesian network classifiers. Machine Learning, 29, 131163.
[10] Cao, Y.Q. and Wu, J.H. (2004) Dynamics of projective adaptive resonance theory model: The foundation of PART algorithm. IEEE Transactions on Neural Networks, 15, 245-260.
[11] Fayyad, U.M. (1991) On the induction of decision trees for multiple concept learning. Doctoral Dissertation, University of Michigan, Ann Arbor.
[12] Ng, A.Y. and Jordan, M.I. (2002) On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. Neural Information Processing Systems, 14, 841.
[13] Breiman, L., Freidman, J., Olshen, R. and Stone, C. (1984) Classification and regression trees. Wadsworth International, California.
[14] Xiao, Y.P., Griffin, M.P., Lake, D.E. and Moorman, J.R. (2010) Nearest neighbor and logistic regression analyses of clinical and heart rate characteristics in the early diagnosis of neonatal sepsis. Medical Decision Making, 30, 258-226. doi:10.1177/0272989X09337791
[15] Wang, Y. (2005) A multinomial logistic regression modeling approach for anomaly intrusion detection. Computer& Security, 24, 662-674.
[16] Giudici, P. (2003) Applied data mining statistical methods for business and industry. Wiley & Sons, Hoboken.
[17] Zhang, J., Jin, R., Yang, Y. and Hauptmann, A.G. (2003) Modified logistic regression: An approximation to SVM and its applications in large-scale text categorization. Proceedings of the 20th International Conference on Machine Learning, Menlo Park, 888-895.
[18] Sulkava, M. and Hollmén, J. (2003) Finding profiles of forest nutrition by clustering of the self-organizing map. Proceedings of the Workshop on Self-Organizing Maps, Kitakyushu, 243-248.
[19] Han, J. and Kamber, M. (2006) Data mining: Concepts and techniques. 2nd Edition, Diane Cerra Publisher, San Francisco.
[20] Witten, I.H. and Frank, E. (2005) Data mining: Practical machine learning tools and techniques. 2nd Edition, Morgan Kaufmann Publisher, Burlington.

  
comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.