Comparing data mining with ensemble classification of breast cancer masses in digital mammograms

Abstract

Medical diagnosis sometimes involves detecting subtle indications of a disease or condition amongst a background of diverse healthy individuals. The amount of information that is available for discovering such indications for mammography is large and has been growing at an exponential rate, due to population wide screening programmes. In order to analyse this information data mining techniques have been utilised by various researchers. A question that arises is: do flexible data mining techniques have comparable accuracy to dedicated classification techniques for medical diagnostic processes? This research compares a model-based data mining technique with a neural network classification technique and the improvements possible using an ensemble approach. A publicly available breast cancer benchmark database is used to determine the utility of the techniques and compare the accuracies obtained