Please use this identifier to cite or link to this item: https://ir.sc.mahidol.ac.th/handle/123456789/113
Title: Hybrid rough sets intelligent system architecture for survival analysis
Authors: Puntip Pattaraintakorn
Cercone, Nick
Kanlaya Naruedomkul
Keywords: Rough sets;Survival analysis;Kaplan-Meier method;Hybrid intelligent systems;Reducts;Soft computing
Issue Date: 2007
Publisher: Springer Berlin Heidelberg
Series/Report no.: Lecture Notes in Computer Science;4400
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.
Description: Transactions on Rough Sets VII, Lecture Notes in Computer Science, Volume 4400, 2007, pp 206-224
URI: http://ir.sc.mahidol.ac.th/handle/123456789/113
ISBN: 978-3-540-71662-4 (Print)
978-3-540-71663-1 (Online)
ISSN: 1861-2059
Appears in Collections:Mathematics: International Proceedings

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