Feature Selection and Predicting CardioVascular Risk
نویسندگان
چکیده
No gold standard exists for assessing the risk of individual patients in cardiovascular medicine. The medical data used for such purposes is, itself, inconsistent over a history of patients at any one clinical site, and not always immediately useable. In this paper the clustering of data using Self Organizing Maps (SOM) is described. This method is an unsupervised neural network developed by Teuvo Kohonen [4]. The SOM is primarily used for the organization and visualization of complex high dimensional data. It produces a mapping of inputs to an output space so that similar patterns of inputs are close together on the map and relatively important inputs take up more space on the map.
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