A foundation of rough sets theoretical and computational hybrid intelligent system for survival analysis

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, melano...

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Bibliographic Details
Published in:Computers & mathematics with applications (1987) 2008-10, Vol.56 (7), p.1699-1708
Main Authors: Pattaraintakorn, Puntip, Cercone, Nick
Format: Article
Language:English
Subjects:
Diabetes
Geriatrics
Intelligent system
Mathematical analysis
Mathematical models
Medical
Patients
Risk
Rough sets
Soft computing
Survival
Survival analysis
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Summary: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.
ISSN:0898-1221
1873-7668
DOI:10.1016/j.camwa.2008.04.030