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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
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)
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)
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)
Tomek, I.: An experiment with the edited nearest-neighbor rule. IEEE Transactions on Systems, Man, and Cybernetics SMC-6(6), 448–452 (1976)
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)
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)
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)
Soto, C.: Model for cervical cancer result prediction. (Ms.D. Thesis in Computer Science). Department of Computer Science, Khon Kaen University, Thailand (2013)
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)
Cohen, W.W.: Fast effective rule induction. In: Proceedings of the Twelfth International Conference on Machine Learning, pp. 115–123. Morgan Kaufmann (1995)
Gaines, B.R., Compton, P.: Induction of ripple-down rules applied to modeling large databases. J. Intell. Inf. Syst. 5(3), 211–228 (1995)
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)
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)
Quinlan, J.R.: C4. 5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)
Huang, G.B., Wang, D.H., Lan, Y.: Extreme learning machines: a survey. Int. J. Mach. Learn. & Cyber. 2(2), 107–122 (2011)
Zong, W., Huang, G.B., Chen, Y.: Weighted extreme learning machine for imbalance learning. J. Neurocomput. 101, 229–242 (2013)
Ganganwar, V.: An overview of classification algorithms for imbalanced datasets. International Journal of Emerging Technology and Advanced Engineering 2(4), 42–47 (2012)
Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Dokduang, K., Chiewchanwattana, S., Sunat, K., Tangvoraphonkchai, V. (2014). A Comparative Machine Learning Algorithm to Predict the Bone Metastasis Cervical Cancer with Imbalance Data Problem. In: Boonkrong, S., Unger, H., Meesad, P. (eds) Recent Advances in Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 265. Springer, Cham. https://doi.org/10.1007/978-3-319-06538-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-06538-0_10
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06537-3
Online ISBN: 978-3-319-06538-0
eBook Packages: EngineeringEngineering (R0)