Early Detection of Prostate Cancer with Classifier Ensembles

We investigate the performance of different classification models and their ability to recognize prostate cancer in an early state. 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. The data sets come from clinical examinations and some of the classification models are already in use to support the urologists in their clinical work.