Abstract
Kidneys play an important role in human body. In essence, a kidney maintains homeostasis and removes harmful materials by making and ejecting a form of urine. Especially 2–3% of humans who have malignancies, also suffered a clear cell renal cell carcinoma (ccRCC) which is one kind of kidney diseases. When diagnosed early, this renal cell carcinoma can be easily treated with some incision surgical method. Nonetheless, some patients who cannot undergo incision surgery need a customized medical service. The ensemble method is usually used to improve the classification performance by combining classifier. For this reason, in this paper, we suggest an implementation of classification algorithm on clinical data to find important clinical factors for ccRCC using an ensemble method and compare the results with a recent work in the literature. The experimental results showed that classification with ensemble methods improved the classification result, especially bagging method.
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References
Koelzer, V.H., Rothschild, S.I., Zihler, D., Wicki, A., Willi, B., Willi, N., Voegeli, M., Cathomas, G., Zippelius, A., Mertz, K.D.: Systemic inflammation in a melanoma patient treated with immune checkpoint inhibitors - an autopsy study. J. Immuno Ther. Cancer 4(1), 13 (2016)
Siegel, R., Ma, J., Zou, Z., Jemal, A.: Cancer statistics, 2014. Cancer J. Clin. 64(1), 9–29 (2014)
Akay, M.F.: Support vector machines combined with feature selection for breast cancer diagnosis. Expert Syst. Appl. 36(2), 3240–3247 (2009)
Lim, K.H., Ryu, K.S., Park, S.H., Shon, H.S., Ryu, K.H.: Short-term mortality prediction of recurrence patients with ST-segment elevation myocardial infarction. J. Korea Soc. Comput. Inf. 17(10), 145–154 (2012)
Choi, N.H., Piao, Y., Li, M., Ryu, K.H.: Comparison of combination of feature selection methods and classification methods for multiclass cancer classification from RNA-seq gene expression data. In: The 7th International Conference FITAT 2014 and 4th International Symposium ISPM 2014, vol. 1, no. 1, pp. 79–81 (2014)
Jung, Y.G., Lee, S.H., Sung, H.J.: Effective diagnostic method of breast cancer data using decision tree. J. Inst. Internet Broadcast. Commun. 10(5), 57–62 (2010)
Baxt, W.G.: Application of artificial neural networks to clinical medicine. Lancet 346(8983), 1135–1138 (1995)
Jayasurya, K., Fung, G., Yu, S., Dehing-Oberije, C., De Ruysscher, D., Hope, A., Dekker, A.L.A.J.: Comparison of Bayesian network and support vector machine models for two-year survival prediction in lung cancer patients treated with radiotherapy. Med. Phys. 37(4), 1401–1407 (2010)
Park, K.H., Ryu, K.S., Shon, H.S., Ryu, K.H.: Performance evaluation of contemporary classification algorithms for high risk patients with clear cell renal carcinoma. In: TSDAA 2015, vol. 1, no. 1 (2015)
Park, K.H., Ryu, K.S., Ryu, K.H.: Determining minimum feature number of classification on clear cell renal cell carcinoma clinical dataset. Int. Conf. Mach. Learn. Cybern. 1(1), 894–898 (2016)
The Cancer Genome Atlas Homepage. http://cancergenome.nih.gov
Namsrai, E., Munkhdalai, T., Namsrai, O.E.: Comparison of feature selection techniques for efficient mining of arrhythmia. In: The 4th International Conference on Frontiers of Information Technology, Applications and Tools, vol. 1, no. 1, pp. 144–148 (2011)
Quinlan, J.R.: Programs for Machine Learning, pp. 1–149. Morgan Kaufmann Publishers Inc., San Francisco (1993)
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)
Li, D.C., Liu, C.W.: A class possibility based kernel to increase classification accuracy for small data set using support vector machines. Expert Syst. Appl. 37(4), 3104–3110 (2010)
Abraham, A.: Artificial neural networks. In: Handbook of Measuring System Design, pp. 901–908 (2005)
Heckerman, D.: Bayesian Networks for Data Mining, pp. 79–119. Kluwer Academic Publishers, Dordrecht (1997)
Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)
Freund, Y., Schapire, R.E.: Experiments with a newboosting algorithm. In: International Conference on Machine Learning, pp. 148–156 (1996)
Acknowledgement
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No. 2017R1A2B4010826), by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-2013-0-00881) supervised by the IITP (Institute for Information & communications Technology Promotion), supported by the KIAT (Korea Institute for Advancement of Technology) grant funded by the Korea Government (MOTIE: Ministry of Trade Industry and Energy). (No. N0002429) in Republic of Korea and also supported by the National Natural Science Foundation of China (61702324) in People’s Republic of China.
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Park, K.H., Ishag, M.I.M., Ryu, K.S., Li, M., Ryu, K.H. (2018). Efficient Ensemble Methods for Classification on Clear Cell Renal Cell Carcinoma Clinical Dataset. In: Nguyen, N., Hoang, D., Hong, TP., Pham, H., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2018. Lecture Notes in Computer Science(), vol 10752. Springer, Cham. https://doi.org/10.1007/978-3-319-75420-8_22
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DOI: https://doi.org/10.1007/978-3-319-75420-8_22
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