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

✍ Scribed by Puntip Pattaraintakorn; Nick Cercone

Publisher: Elsevier Science
Year: 2008
Tongue: English
Weight: 545 KB
Volume: 56
Category: Article
ISSN: 0898-1221
DOI: 10.1016/j.camwa.2008.04.030

No coin nor oath required. For personal study only.

✦ Synopsis

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.

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