Input and data selection applied to heart disease diagnosis

In this paper we present an application of data and input selection to a heart disease diagnosis problem. We approach the problem by using a modified LVQ scheme that selects a subset of the training data points to update the prototypes. The main model goal is to identify patients with relevant coronary vessels obstruction. The selected subset provides an interesting interpretation. We associate this methodology with a weighted norm, instead of the Euclidean, in order to establish different levels of importance for the input attributes. Again, interesting interpretation arises concerning the relevance of the input attributes.