Breast Cancer Classification Using an Adapted Bump Hunting Algorithm

The Patient Rule Induction Method (PRIM) is a data mining technique used for identifying patterns in datasets, particularly focusing on discovering regions of the chosen input space where the response variable is unusually high or low. It falls in the subgroup Discovery field, where finding small groups is more relevant for the explainability of the results, although it is not a classification technique per se. In this paper, we introduce a new Framework for the breast cancer classification based on PRIM. This new method involves, first, the random choice of different input space for each class label. Second, the organization and the pruning of the rules using the Metarules. And finally, it also includes the proposition of a way to handle the class overlapping and, hence, define the final classifier. The Framework is tested on five real-live breast cancer datasets and compared to 3 often used algorithms for breast cancer classification: XG Boost, Logistic Regression and Random Forest. Across the four metrics and datasets, both our PRIM-based Framework and Random Forest demonstrate robust performance, with our framework showing notable accuracy and recall. XGBoost maintains strong F1-scores across the board, indicating balanced precision and recall. In the other hand Logistic Regression, while competent, generally underperforms compared to the other algorithms, especially in terms of accuracy and recall, achieving 94.1% accuracy against 96.8% and 85.4% recall against 94.2% for the PRIM-based framework on the Wisconsin dataset.