# Breast Cancer Classification Using an Adapted Bump-Hunting Algorithm
## 4 Results
### Empirical Results
In medical applications, where minimizing missed diagnoses and needless procedures is essential, this balance is essential. The PRIM framework consistently performs above 94 percent across all datasets according to the F1-scores, which give a harmonic mean of precision and recall. The PRIM framework's robustness in handling complex medical data is demonstrated by its 96.9 percent F1-score in the NKI dataset, which is especially impressive given that it deals with high-dimensional data (1570 features). The consistency of the PRIM framework's performance across datasets with different characteristics is what makes it so remarkable. Whether working with high-dimensional genetic data (NKI), imaging results (Mammogram masses), survival data (SEER), or clinical measurements (Wisconsin), the PRIM framework consistently maintains strong performance metrics that are competitive with, and occasionally surpass, those of well-known algorithms like Random Forest and XGBoost. Even though the PRIM framework is sometimes marginally better than traditional ensemble methods, the PRIM framework's performance is still dependable and clinically relevant. When taking into account the PRIM framework's interpretability advantage over \"black box\" techniques like Random Forest and XGBoost, this becomes even more important. The framework is especially useful for medical applications where it is essential for patients and healthcare providers to comprehend the decision-making process because it can maintain such high performance while producing results that are interpretable. The number of rules generated per class depends on the dimensionality of the data. The more dimensions we have, the more random feature combinations we obtain and the more subgroups we mine. For the NKI dataset for instance, even if the number of instances is 272, the number of attributes is very high at 1570, so the number of subgroups selected for the final classifier before going through the metarules processed can be perceived as high. One should keep in mind that our approach aims at finding the subgroups, so the high number is totally normal in this case since the boxes are very dense and small in comparison with traditional classification procedures. We gave the number of rules per class before and after the metarules for transparency reasons and also to provide the experts with the whole ruleset if they wish to analyze the output carefully in case there is a new set of features that define a subgroup they never suspected. In the metarules process, as stated in Section 3, the association between the rules shows the containment of rules and overlaps, which makes the number considerably decrease in some datasets, like the rules for class 1 in the SEER dataset, which went from 72 to 44 after the metarules. This analysis, displayed in Table 5, shows that the higher the dimensions, the more the rules found will not be contained or overlapped because the framework selects different subspaces each time. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{4}{c}{**Number of Rules Per Class**} \\ \cline{2-5}  & \multicolumn{2}{c}{**Before the Metarules**} & \multicolumn{2}{c}{**After the Metarules**} \\ \cline{2-5}  & **0** & **1** & **0** & **1** \\ \hline
**Wisconsin** & 36 & 45 & 19 & 34 \\
**SEER** & 28 & 72 & 15 & 44 \\
**IYSP1-clinica** & 5 & 12 & 5 & 11 \\
**Mammographic masses** & 14 & 12 & 9 & 9 \\
**NKI dataset** & 38 & 54 & 12 & 33 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Number of rules generated by the framework before and after the metarules step. The difference in the datasets provides a good insight into the performance of the framework given that the datasets are very different from each other.