KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Author: search.filters.author.Cercone, NickSubject: search.filters.subject.Soft computing

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    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.