# Breast Cancer Classification Using an Adapted Bump-Hunting Algorithm
###### Abstract  
The Patient Rule Induction Method 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 breast cancer classification based on the PRIM. This new method involves, first, the random choice of different input spaces for each class label; second, the organization and pruning of the rules using 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-life breast cancer datasets compared to three 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. On 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. p 1 (202  
Even though scientists have worked on improving the accuracy of ML models, explainability remains necessary to help practitioners in their decision-making process. Current research has focused on making the models understandable and usable [4]. In [5], the authors give a review on explainable artificial intelligence (AI) and accentuate the focus lately on this field involving the interaction with humans. In this context, it is essential to focus on both sides: the high accuracy of the model and the explainable and interpretable results. One of the approaches that has been constructed to deal with research in an input space of interesting groups and behave well with high-dimensional data is bump hunting. Introduced by [PERSON] and [PERSON] [3], the Patient Rule Induction Method is a bump-hunting algorithm that consists of searching at each iteration for the most optimal rule. Since it peels one dimension at a time, it constructs, at the end, a rule shaped like a box, hence the term box induction used in the literature. The PRIM is not a classification method. The output is a set of rules corresponding to one class. It is, therefore, a powerful data mining procedure to explore the dataset. We have chosen to adapt this method in a way that builds an interpretable and explainable classifier for different types of breast cancer classification problems. To this end, we introduce, in this paper, a framework for breast cancer classification based on an adaptation of the Patient Rule Induction Method handling both categorical and numeric features based on the Random PRIM Classifier developed in [6]. The suggested framework allows us to build classification models that are as accurate as cutting-edge ensemble techniques while still being easy to understand and explain. Indeed, discovering new subgroups in the data space could lead, in this medical field, to new insights into breast cancer diagnosis and prognosis problems. In the proposed framework for breast cancer classification, we run, for each class, several iterations of the PRIM algorithm on random subsets of features to find all the rules. Then, cross-validation is used to select the rules to produce the correct classifier for each dataset. Finally, the resulting classification rules are organized and pruned using metarules [7]. At this level, we identify containment between the rules of the same class and identify the overlapping regions between the rules representing different classes. The rest of the paper is organized as follows.