Post-Operative Patient Data Set
Below are papers that cite this data set, with context shown.
Papers were automatically harvested and associated with this data set, in collaboration with Rexa.info.
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Petri Kontkanen and Jussi Lahtinen and Petri Myllymäki and Henry Tirri. Unsupervised Bayesian visualization of high-dimensional data. KDD. 2000.
or in the relative sense with respect to the default classification accuracy (as with the Postoperative Patient data set), the class labeled colored images are somewhat blurred. Nevertheless, wewould like to emphasize that this does not mean that the unsupervised visualization technique has failed'' in these cases,
Art B. Owen. Tubular neighbors for regression and classification. Stanford University. 1999.
The model it chose ignored three of the input variables, was linear in four of the others and piece-wise linear in one input variable. 29 7.6 Other data 7.6.1 post-operative data This post-operative data set comes from the Irvine repository. The goal is to predict whether a patient will be sent to the hospital floor or sent home. The training data also include two patients who were sent to the intensive
Glenn Fung and Sathyakama Sandilya and R. Bharat Rao. Rule extraction from Linear Support Vector Machines. Computer-Aided Diagnosis & Therapy, Siemens Medical Solutions, Inc.
The first experiment relates to the publicly available WDBC dataset that consists of 683 patient data The classification task associated with this dataset is to diagnose breast masses based solely on a Fine Needle Aspiration (FNA). Doctors identified nine visually