Individual and Collective Prognostic Prediction

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dc.contributor.author	Wolberg, W.H.
dc.contributor.author	Mangasarian, O.L.
dc.contributor.author	Street, W. Nick
dc.date.accessioned	2013-04-29T18:42:45Z
dc.date.available	2013-04-29T18:42:45Z
dc.date.issued	1996-01-04
dc.identifier.citation	96-01	en
dc.identifier.uri	http://digital.library.wisc.edu/1793/65414
dc.description.abstract	The prediction of survival time or recurrence time is an important learning problem in medical domains. The Recurrence Surface Approximation (RSA) method is a natural, effective method for predicting recurrence times using censored input data. This paper introduces the Survival Curve RSA (SC-RSA), an extension to the RSA approach which produces accurate predicted rates of recurrence, while maintaining accuracy on individual predicted recurrence times. The method is applied to the problem of breast cancer recurrence using two different datasets	en
dc.title	Individual and Collective Prognostic Prediction	en
dc.type	Technical Report	en

Math Prog Technical Reports
Math Prog Technical Reports Archive for the Department of Computer Sciences at the University of Wisconsin-Madison