# Breast Cancer Classification Using an Adapted Bump-Hunting Algorithm
## 4 Results
### Empirical Results  
In this section, we present empirical results to determine which model is the best for each dataset to situate our framework, followed by the limitations of the study to light the way for future work and improve the framework. Tables 3 and 4 summarize the results of the empirical comparison. They display the datasets and the four scores that are measured for the classifiers produced by the Random Forest, XGBoost, Logistic Regression, and R-PRIM frameworks. Three scores (recall, precision, F1-score) reflect the importance of classifying positive examples along with accuracy, as seen in Figure 7, which is a widely used score for evaluating the overall effectiveness of classifiers in classification problems with approximately similar proportions of data samples for each class label. Additionally, Figure 8 provides the ROC and AUC for every dataset. The results given in Tables 3 and 4 show that the classifiers produced by the PRIM-based framework perform as well as those produced by the three other algorithms. Being able to detect isolated regions in the input spaces, the resulting classifier handled the imbalanced data quite well since the boxes found were small. Figure 7 offers a visualization of the performance of the four algorithms on the seven datasets for each measure, Figure 7a showing the accuracy, Figure 7b the recall, Figure 7c the precision, and Figure 7d the F1-score. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Accuracy**} & \multicolumn{4}{c}{**F1-Score**} \\ \hline
**Datasets** & **RF** & **XGB** & **LG** & **R-PRIM-CI** & **RF** & **XGB** & **LG** & **R-PRIM-CI** \\ \hline Wisconsin & **97.6** & 95.4 & 94.1 & 96.8 & 96 & **97.3** & 89.9 & 94.9 \\ \hline SEER & 94.4 & 97.5 & 88.7 & **98.4** & 96.7 & **97.3** & 87.4 & 96.3 \\ \hline TYSP1-clinica & **98.7** & 96.3 & 89.8 & 95.3 & **98** & 95.3 & 88.2 & 94.7 \\ \hline Mammographic masses & 95.8 & **98.6** & 92.4 & 97.2 & **97.6** & 97.4 & 92.8 & 96.1 \\ \hline NKI dataset & **98** & 97.5 & 85.6 & 95.6 & **97.8** & 95.6 & 93.7 & 96.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the F1-score and accuracy of every algorithm on the seven datasets in %. Across all datasets, R-PRIM-CL's performance exhibits exceptional consistency. The PRIM framework outperforms both Random Forest and XGBoost in terms of accuracy, achieving 98.4% in the SEER dataset, which is especially noteworthy. This is particularly noteworthy because, with 4024 samples, SEER is the largest dataset, indicating that the PRIM framework scales well with larger medical datasets. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Precision**} & \multicolumn{4}{c}{**Recall**} \\ \hline
**Datasets** & **RF** & **XGB** & **LG** & **R-PRIM-Cl** & **RF** & **XGB** & **LG** & **R-PRIM-Cl** \\ \hline Wisconsin & **98.2** & 98 & 94.8 & 95.6 & 93.9 & 96.7 & 85.4 & **94.2** \\ \hline SEER & 95.6 & **97.6** & 88.1 & 97.1 & **98** & 97.2 & 86.7 & 95.6 \\ \hline IYSP1-clinica & **98.9** & 95.2 & 86.5 & 94.6 & **97.2** & 95.4 & 89.9 & 94.8 \\ \hline Mammographic masses & **97.7** & 96.3 & 92.4 & 96.3 & 97.5 & **98.7** & 93.2 & 95.8 \\ \hline NKI dataset & **97.9** & 94.5 & 93.3 & 96.7 & **97.8** & 96.7 & 94.1 & 97.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the precision and recall of every algorithm on the seven datasets in %. Figure 7: Visualization of the four measures obtained in the experiment: (**a**) the accuracy of each model; (**b**) the recall of each model; (**c**) the precision of each model; and (**d**) the F1-score of each model. Figure 8: Visualization of the ROC and AUC for each dataset. The PRIM framework exhibits well-balanced performance when looking at precision and recall metrics. For instance, the PRIM framework maintains a good balance between false positives and false negatives in the Wisconsin dataset, achieving 95.6 percent precision and 94.2 percent recall.