Abstract
In this work a 5-year survival prediction model was developed for colon cancer using machine learning methods. The model was based on the SEER dataset which, after preprocessing, consisted of 38,592 records of colon cancer patients. Survival prediction models for colon cancer are not widely and easily available. Results showed that the performance of the model using fewer features is close to that of the model using a larger set of features recommended by an expert physician, which indicates that the first may be a good compromise between usability and performance. The purpose of such a model is to be used in Ambient Assisted Living applications, providing decision support to health care professionals.
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.
Similar content being viewed by others
References
Al-Bahrani, R., Agrawal, A., Choudhary, A.: Colon cancer survival prediction using ensemble data mining on seer data. In: 2013 IEEE International Conference on Big Data, pp. 9–16 (2013)
Bradley, A.P.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30(7), 1145–1159 (1997)
Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)
Bush, D.M., Michaelson, J.S.: Derivation: Nodes + PrognosticFactors Equation for Colon Cancer. Tech. rep. (2009)
Chang, G.J., Hu, C.Y., et al.: Practical application of a calculator for conditional survival in colon cancer. Journal of Clinical Oncology 27(35), 5938–5943 (2009)
Džeroski, S., Ženko, B.: Is combining classifiers with stacking better than selecting the best one? Machine Learning 54(3), 255–273 (2004)
Ferlay, J., Soerjomataram, I., Ervik, M., et al.: Globocan 2012: Estimated cancer incidence, mortality and prevalence worldwide in 2012 (2012). http://globocan.iarc.fr (last visited on December 27, 2015)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)
Ganganwar, V.: An overview of classification algorithms for imbalanced datasets. Int. J. Emerg. Technol. Adv. Eng 2(4), 42–47 (2012)
Kittler, J.: Combining classifiers: A theoretical framework. Pattern Analysis and Applications 1(1), 18–27 (1998)
Klepac, G., Klepac, G., et al.: Developing Churn Models Using Data Mining Techniques and Social Network Analysis, 1st edn. IGI Global, Hershey (2014)
National Cancer Institute: Surveillance, epidemiology and end results program (2015). http://seer.cancer.gov/data/ (last visited on October 01, 2015)
Oliveira, T., Leão, P., Novais, P., Neves, J.: Webifying the computerized execution of clinical practice guidelines. In: Bajo Perez, J., Corchado, J.M., et al. (eds.) The PAAMS Collection SE - 18, Advances in Intelligent Systems and Computing, vol. 293, pp. 149–156. Springer International Publishing (2014)
RapidMiner: Rapidminer documentation: Operator reference guide (2016). http://docs.rapidminer.com/studio/operators/ (last visited on March 01, 2016)
Refaeilzadeh, P., Tang, L., Liu, H.: Cross-validation. In: Liu, L., Özsu, M. (eds.) Encyclopedia of Database Systems, pp. 532–538. Springer US (2009)
Yamauchi, M., Lochhead, P., Morikawa, T., et al.: Colorectal cancer: a tale of two sides or a continuum? Gut 61(6), 794–797 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Silva, A., Oliveira, T., Novais, P., Neves, J., Leão, P. (2016). Developing an Individualized Survival Prediction Model for Colon Cancer. In: Lindgren, H., et al. Ambient Intelligence- Software and Applications – 7th International Symposium on Ambient Intelligence (ISAmI 2016). ISAmI 2016. Advances in Intelligent Systems and Computing, vol 476. Springer, Cham. https://doi.org/10.1007/978-3-319-40114-0_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-40114-0_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-40113-3
Online ISBN: 978-3-319-40114-0
eBook Packages: EngineeringEngineering (R0)