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Author: search.filters.author.Cercone, Nick

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    A foundation of rough sets theoretical and computational hybrid intelligent system for survival analysis
    (2008-10-01)
    Pattaraintakorn, Puntip
    ;
    Cercone, Nick
    What do we (not) know about the association between diabetes and survival time? Our study offers an alternative mathematical framework based on rough sets to analyze medical data and provide epidemiology survival analysis with risk factor diabetes. We experiment on three data sets: geriatric, melanoma and Primary Biliary Cirrhosis. A case study reports from 8547 geriatric Canadian patients at the Dalhousie Medical School. Notification status (dead or alive) is treated as the censor attribute and the time lived is treated as the survival time. The analysis result illustrates diabetes is a very significant risk factor to survival time in our geriatric patients data. This paper offers both theoretical and practical guidelines in the construction of a rough sets hybrid intelligent system, for the analysis of real world data. Furthermore, we discuss the potential of rough sets, artificial neural networks (ANNs) and frailty index in predicting survival tendency. © 2008 Elsevier Ltd. All rights reserved.
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    Integrating rough set theory and medical applications
    (2008-04-01)
    Pattaraintakorn, Puntip
    ;
    Cercone, Nick
    Medical science is not an exact science in which processes can be easily analyzed and modeled. Rough set theory has proven well suited for accommodating such inexactness of the medical profession. As rough set theory matures and its theoretical perspective is extended, the theory has been also followed by development of innovative rough sets systems as a result of this maturation. Unique concerns in medical sciences as well as the need of integrated rough sets systems are discussed. We present a short survey of ongoing research and a case study on integrating rough set theory and medical application. Issues in the current state of rough sets in advancing medical technology and some of its challenges are also highlighted. © 2007 Elsevier Ltd. All rights reserved.
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    Hybrid rough sets-population based system
    (2007-01-01)
    Pattaraintakorn, Puntip
    ;
    Cercone, Nick
    The integration of mathematical and statistical data analysis research can engender a novel and better approach, especially for survival analysis. This paper is devoted to Professor Pawlak and his ideas about rough sets and its applications. We propose MULTIHYRIS, an alternative hybrid intelligent system with a rough sets and population based approach for survival analysis. MULTIHYRIS is designed to increase the versatility and efficiency of survival analysis techniques. The MULTIHYRIS architecture incorporates mathematics - rough sets (with discernibility relations and individual patient consideration) - with statistics - Kaplan-Meier and Cox methods (with population estimates). The central idea behind MULTIHYRIS is to perform univariate analysis by using rough sets, database management and the Kaplan-Meier method with soft computing. All results from the univariate analysis are subsequently used in further mulitvariate analysis. In this stage, we provide two optional approaches to serve different requirements; rough sets integrated with database management and the Cox method. The former approach is able to produce decision rules while the latter generates a Cox model. Furthermore, set operations are used to unite these two outcomes and generate new reducts - hybrid reducts based on our rough sets-population based system. The informativeness of the rules and models can be verified within this analysis by validation processes and statistical tests. To demonstrate MULTIHYRIS, we have implemented it on a real-world geriatric data set, collected from the Dalhousie Medical School. © Springer-Verlag Berlin Heidelberg 2007.
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    Hybrid rough sets intelligent system architecture for survival analysis
    (2007-01-01)
    Pattaraintakorn, Puntip
    ;
    Cercone, Nick
    ;
    Naruedomkul, Kanlaya
    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. © Springer-Verlag Berlin Heidelberg 2007.
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    Rule evaluations, attributes, and rough sets: Extension and a case study
    (2007-01-01)
    Li, Jiye
    ;
    Pattaraintakorn, Puntip
    ;
    Cercone, Nick
    Manually evaluating important and interesting rules generated from data is generally infeasible due to the large number of rules extracted. Different approaches such as rule interestingness measures and rule quality measures have been proposed and explored previously to extract interesting and high quality association rules and classification rules. Rough sets theory was originally presented as an approach to approximate concepts under uncertainty. In this paper, we explore rough sets based rule evaluation approaches in knowledge discovery. We demonstrate rule evaluation approaches through a real-world geriatric care data set from Dalhousie Medical School. Rough set based rule evaluation approaches can be used in a straightforward way to rank the importance of the rules. One interesting system developed along these lies in HYRIS (HYbrid Rough sets Intelligent System). We introduce HYRIS through a case study on survival analysis using the geriatric care data set. © Springer-Verlag Berlin Heidelberg 2007.
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    Rule analysis with rough sets theory
    (2006-11-22)
    Pattaraintakorn, Puntip
    ;
    Cercone, Nick
    ;
    Naruedomkul, Kanlaya
    Postprocessing is a significant step in the data analysis process which is often ignored or glossed over. Once we have a large set of generated rules, how can we elicit the sufficient and necessary rules? In this paper, we propose an alternative approach for decision rule learning with rough sets theory in the postprocessing step called 'ROSERULE'. Essentially, we introduce rule reducts, a sufficient and necessary part which preserves classification of the rule universe, as a rough sets tool for rule analysis. ROSERULE learns and analyzes from the rule set to generate rule reducts which can be used to reduce the number of the rules. This is in contrast to common rule analysis which simply performs rule selection. We illustrate the performance of ROSERULE with several case studies; melanoma, primary biliary cirrhosis, pneumonia and a real-world case study, geriatric data sets. ROSERULE is run on these data sets and the result are a reduced number of rules that successfully preserve the original classification. © 2006 IEEE.