Evolutionary identification of cancer predictors using clustered data
Stephan Winkler, Michael Affenzeller, Herbert Stekel · 2013
In this paper we discuss the effects of using pre-clustered data on the identification of estimation models for cancer diagnoses. Based on patients' data records including standard blood parameters, tumor markers, and information about the diagnosis of tumors, the goal is to identify mathematical models for estimating cancer diagnoses. We have applied a hybrid clustering and classification approach that first identifies data clusters (using standard patient data and tumor markers) and then learns prediction models on the basis of these data clusters. In the empirical section we analyze the clusters of patient data samples formed using k-means clustering: The optimal number of clusters is identified, and we investigate the homogeneity of these clusters.