Advertisement

A Comparative Machine Learning Algorithm to Predict the Bone Metastasis Cervical Cancer with Imbalance Data Problem

  • Kasama Dokduang
  • Sirapat Chiewchanwattana
  • Khamron Sunat
  • Vorachai Tangvoraphonkchai
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 265)

Abstract

This paper attempted to develop and validate a tool to predict the immediate results of radiation on bone metastasis in cervical cancer cases. Cases of bone metastasis in cervical cancer are based on radiation treatment data, which is imbalanced. This imbalanced data is a challenge among the researchers in data mining, called class imbalance learning (CIL) and has lead to difficulties in machine learning and a reduction in the classifier performance. In this paper, we compared several algorithms to deal with the data imbalance classification problem using the synthetic minority over-sampling technique (SMOTE) used to drive classification models: Ant-Miner, RIPPER, Ridor, PART, ADTree, C4.5, ELM and Weighted ELM using Accuracy, G-mean and F-measure to evaluate performance. The results of this paper show that the RIPPER algorithm outperformed the other algorithms in Accuracy and F-measure, but weighted ELM outperformed other algorithms by G-mean. This may be useful when evaluating clinical assessments.

Keywords

cervical cancer classification algorithm radiotherapy imbalance data machine learning metastasis 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Nartthanarung, A., Thanapprapasr, D.: Comparison of Outcomes for Patients With Cervical Cancer Who Developed Bone Metastasis After the Primary Treatment With Concurrent Chemoradiation Versus Radiation Therapy Alone. Int. J. Gynecol. Cancer 20(8), 1386–1390 (2010)Google Scholar
  2. 2.
    Thanapprapasr, D., Nartthanarung, A., Likittanasombut, P., Na Ayudhya, N.I., Charakorn, C., Udomsubpayakul, U., Subhadarbandhu, T., Wilailak, S.: Bone Metastasis in Cervical Cancer Patients over a 10-Year Period. Int. J. Gynecol. Cancer 20(3), 373–378 (2010)CrossRefGoogle Scholar
  3. 3.
    Kamsa-ard, S., Tangvorapongchai, V., Krusun, S., Sriamporn, S., Suwanrungruang, K., Mahaweerawat, S., Pomros, P.: A model to predict the immediate results of radiation on cervix cancer. KKU Res. J. 13(7), 851–865 (2008)Google Scholar
  4. 4.
    Ochi, T., Murase, K., Fujii, T., Kawamura, M., Ikezoe, J.: Survival prediction using artificial neural networks in patients with uterine cervical cancer treated by radiation therapy alone. Int. J. Clin. Oncol. 7(5), 294–300 (2002)Google Scholar
  5. 5.
    Tomek, I.: An experiment with the edited nearest-neighbor rule. IEEE Transactions on Systems, Man, and Cybernetics SMC-6(6), 448–452 (1976)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Kubat, M., Matwin, S.: Addressing the curse of imbalanced training sets: one-sided selection. In: Fisher, D.H. (ed.) ICML, vol. 97, pp. 179–186. Morgan Kaufmann (1997)Google Scholar
  7. 7.
    Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Int. Res. 16(1), 321–357 (2002)zbMATHGoogle Scholar
  8. 8.
    Chawla, N.V., Japkowicz, N., Kotcz, A.: Editorial: special issue on learning from imbalanced data sets. ACM SIGKDD Explorations Newsletter 6(1), 1–6 (2004)CrossRefGoogle Scholar
  9. 9.
    Soto, C.: Model for cervical cancer result prediction. (Ms.D. Thesis in Computer Science). Department of Computer Science, Khon Kaen University, Thailand (2013)Google Scholar
  10. 10.
    Parpinelli, R.S., Lopes, H.S., Freitas, A.A.: Data mining with an ant colony optimization algorithm. IEEE Transactions on Evolutionary Computation 6(4), 321–332 (2002)CrossRefGoogle Scholar
  11. 11.
    Cohen, W.W.: Fast effective rule induction. In: Proceedings of the Twelfth International Conference on Machine Learning, pp. 115–123. Morgan Kaufmann (1995)CrossRefGoogle Scholar
  12. 12.
    Gaines, B.R., Compton, P.: Induction of ripple-down rules applied to modeling large databases. J. Intell. Inf. Syst. 5(3), 211–228 (1995)CrossRefGoogle Scholar
  13. 13.
    Frank, E., Witten, I.H.: Generating accurate rule sets without global optimization. In: Shavlik, J.W. (ed.) Proceedings of the Fifteenth International Conference on Machine Learning, ICML 1998, pp. 144–151. Morgan Kaufmann Publishers Inc., San Francisco (1998)Google Scholar
  14. 14.
    Freund, Y., Mason, L.: The alternating decision tree learning algorithm. In: Bratko, I., Dzeroski, S. (eds.) Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), pp. 124–133. Morgan Kaufmann Publishers Inc., San Francisco (1999)Google Scholar
  15. 15.
    Quinlan, J.R.: C4. 5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)Google Scholar
  16. 16.
    Huang, G.B., Wang, D.H., Lan, Y.: Extreme learning machines: a survey. Int. J. Mach. Learn. & Cyber. 2(2), 107–122 (2011)CrossRefGoogle Scholar
  17. 17.
    Zong, W., Huang, G.B., Chen, Y.: Weighted extreme learning machine for imbalance learning. J. Neurocomput. 101, 229–242 (2013)CrossRefGoogle Scholar
  18. 18.
    Ganganwar, V.: An overview of classification algorithms for imbalanced datasets. International Journal of Emerging Technology and Advanced Engineering 2(4), 42–47 (2012)Google Scholar
  19. 19.
    Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kasama Dokduang
    • 1
  • Sirapat Chiewchanwattana
    • 1
  • Khamron Sunat
    • 1
  • Vorachai Tangvoraphonkchai
    • 2
  1. 1.Department of Computer Science, Faculty of ScienceKhon Kaen UniversityKhon KaenThailand
  2. 2.Department of Radiology, Faculty of MedicineKhon Kaen UniversityKhon KaenThailand

Personalised recommendations