Classification performance of data mining algorithms applied to breast cancer data

In this paper, we study how several classification algorithms perform when applied to a breast cancer dataset. The challenge is to develop models for computer-aided detection (CAD), capable to classify, at early stages, masses spotted in X-ray images. The dataset was available at KDD CUP 2008. The imbalanced nature of the dataset and its high-dimensional feature space poses problems to the modelling that are tackled using dimension reduction techniques. The algorithms are compared using the area under the curve (AUC) of the receiver operating characteristic curve (ROC) between true-and false-positive rates (TPR and FPR). Other metrics, such as patient sensitivity and FPR are used and discussed. We find that Naive Bayes classifier achieved the best performance irrespective of the combination of datasets and allow controlled trade-offs between false positives and negatives.