Abstract
Survival analysis challenges researchers because of two issues. First, in practice, the studies do not span wide enough to collect all survival times of each individual patient. All of these patients require censor variables and cannot be analyzed without special treatment. Second, analyzing risk factors to indicate the significance of the effect on survival time is necessary. Hence, we propose “Enhanced Hybrid Rough Sets Intelligent System Architecture for Survival Analysis” (Enhanced HYRIS) that can circumvent these two extra issues.
Given the survival data set, Enhanced HYRIS can analyze and construct a life time table and Kaplan-Meier survival curves that account for censor variables. We employ three statistical hypothesis tests and use the p−value to identify the significance of a particular risk factor. Subsequently, rough set theory generates the probe reducts and reducts. Probe reducts and reducts include only a risk factor subset that is large enough to include all of the essential information and small enough for our survival prediction model to be created. Furthermore, in the rule induction stage we offer survival prediction models in the form of decision rules and association rules. In the validation stage, we provide cross validation with ELEM2 as well as decision tree. To demonstrate the utility of our methods, we apply Enhanced HYRIS to various data sets: geriatric, melanoma and primary biliary cirrhosis (PBC) data sets. Our experiments cover analyzing risk factors, performing hypothesis tests and we induce survival prediction models that can predict survival time efficiently and accurately.
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
Larry, M.R.: Hybrid Intelligent System. Kluwer Academic Publishers, Boston (1995)
Pattaraintakorn, P., Cercone, N., Naruedomkul, K.: Hybrid intelligent systems: selecting attributes for soft-computing analysis. In: Proceedings of the 29th Annual International Computer Software and Applications Conference (COMPSAC2005), vol. 1, Edinburgh, Scotland, UK, pp. 319–325. IEEE Computer Society Press, Los Alamitos (2005)
Pattaraintakorn, P., Cercone, N., Naruedomkul, K.: Selecting attributes for soft-computing analysis in hybrid intelligent systems. In: Ślęzak, D., et al. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3642, pp. 698–708. Springer, Heidelberg (2005)
Kaplan, E.L., Meier, P.: Nonparametric estimation from incomplete observations. Journal of the American Statistical Association 53, 457–481 (1958)
Cox, D.R.: The analysis of exponentially distributed life-times with two types of failure. Journal of the Royal Statistical Society 21, 411–412 (1959)
Peto, R., Peto, J.: Asymptotically efficient rank invariant procedures. Journal of the Royal Statistical Society 135, 185–207 (1972)
Gehan, E.A.: A Generalized Wilcoxon test for comparing arbitrarily singly-censored data. Biometrika 52, 203–223 (1965)
Tarone, R.E., Ware, J.: On distribution-free tests for equality of survival distributions. Biometrika 64, 156–160 (1977)
Bazan, J., et al.: Rough set approach to the survival analysis. In: Alpigini, J.J., et al. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 522–529. Springer, Heidelberg (2002)
Bazan, J., et al.: Searching for the complex decision reducts: the case study of the survival analysis. In: Zhong, N., et al. (eds.) ISMIS 2003. LNCS (LNAI), vol. 2871, pp. 160–168. Springer, Heidelberg (2003)
Pawlak, Z.: Rough sets. Theoretical aspects of reasoning about data. Kluwer Academic Publishers, Dordrecht (1991)
Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Slowinski, R. (ed.) Intelligent Decision Suppport - Handbook of Applications and Advances of the Rough Sets Theory, pp. 331–362. Kluwer Academic Publishers, Dordrecht (1992)
Nguyen, S.H., Nguyen, H.S.: Some efficient algorithms for rough set methods. In: Proceedings of the Sixth International Conference on Information Processing and Management of Uncertainty Knowledge Based Systems (IPMU1996), vol. 3, Granada, Spain, pp. 1451–1456 (1996)
Nguyen, H.S.: Approximate Boolean reasoning approach to rough sets and data mining. In: Ślęzak, D., et al. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3642, pp. 12–22. Springer, Heidelberg (2005)
Wroblewski, J.: Theoretical foundations of order-based genetic algorithms. Fundamenta Informaticae 28, 423–430 (1996)
Bazan, J., et al.: Rough set algorithms in classification problems. In: Polkowski, L., Lin, T.Y., Tsumoto, S. (eds.) Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems. Studies in Fuzziness and Soft Computing, vol. 56, pp. 49–88. Springer, Heidelberg (2000)
Hu, X., Han, J., Lin, T.Y.: A new rough sets models based on database systems. Fundamenta Informaticae 59(2-3), 1–18 (2004)
An, A., Cercone, N.: ELEM2: a learning system for more accurate classifications. In: Mercer, R.E. (ed.) Canadian AI 1998. LNCS, vol. 1418, pp. 426–441. Springer, Heidelberg (1998)
Pattaraintakorn, P., Cercone, N., Naruedomkul, K.: Rule analysis with rough sets theory. In: Proceedings of the IEEE International Conference on Granular Computing (IEEEGrC2006), Atlanta, USA, pp. 582–585. IEEE Computer Society Press, Los Alamitos (2006)
Pattaraintakorn, P., Cercone, N., Naruedomkul, K.: Rule learning: ordinal prediction based on rough set and soft–computing. Applied Mathematics Letters 19, 1300–1307 (2006)
Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this chapter
Cite this chapter
Pattaraintakorn, P., Cercone, N., Naruedomkul, K. (2007). Hybrid Rough Sets Intelligent System Architecture for Survival Analysis. In: Peters, J.F., Skowron, A., Marek, V.W., Orłowska, E., Słowiński, R., Ziarko, W. (eds) Transactions on Rough Sets VII. Lecture Notes in Computer Science, vol 4400. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71663-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-540-71663-1_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71662-4
Online ISBN: 978-3-540-71663-1
eBook Packages: Computer ScienceComputer Science (R0)