Classification models for early detection of prostate cancer

Abstract

We investigate the performance of different classification models and their ability to recognize prostate cancer in an early stage. We build ensembles of classification models in order to increase the classification performance. We measure the performance of our models in an extensive cross-validation procedure and compare different classification models. The datasets come from clinical examinations and some of the classification models are already in use to support the urologists in their clinical work.

Figure 1

A scatterplot matrix of the data. Each box shows a pair of variables and the cases are color-coded, a red cross marks PCa, and a blue circle non-PCa. The DRE is a binary variable (suspicious or nonsuspicious).

Figure 2

For every partition of the cross-validation, the data is divided in a training and a test set. The performance of each ensemble model was assessed on validation set which was initially removed and never included in model training.

Figure 3

A sketch of a classification tree, wherein the leaves represent classes and the branches represent conjunctions of features that lead to those classes.