# Breast Cancer Classification Using an Adapted Bump-Hunting Algorithm
## 4 Results
### Empirical Results
The Mammographic masses dataset contains low-dimensional data with only six attributes to choose random combinations from for the search space, but they are nearly balanced data that give us a high accuracy for the model of 97.2%, while the NKI dataset is more complex to work with because it only has about 272 cases for 1570 attributes, which make it a small, high-dimensional dataset where the framework also performed well with an accuracy of 95.6%. After going over the fundamentals of our model's performance, we now focus on the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) in Figure 8 to assess the model's overall efficacy and discriminative capacity. The ROC curves for the Wisconsin dataset in Figure 8a demonstrate superior classification performance for every algorithm, with AUC values above 0.95. XGBoost and Random Forest perform especially well, with AUC values between 0.98, suggesting almost flawless differentiation between benign and malignant cases. Closely behind is the PRIM classifier, while Logistic Regression exhibits marginally poorer discriminative ability despite still doing well. The high AUC values imply that these models would be trustworthy in supporting clinical breast cancer diagnosis. All classifiers consistently perform well when it comes to the SEER dataset in Figure 8b. With AUC values greater than 0.97, XGBoost and the PRIM framework demonstrate exceptional discrimination capabilities. Similar performance levels are maintained by the Random Forest algorithm, while Logistic Regression again performs marginally worse but still maintains clinically meaningful accuracy above 0.90. For survival prediction, it is especially crucial that the curves in the high-sensitivity region group tightly. The models continue to perform well for the ISPY1-clinica dataset in Figure 8c, which has a smaller sample size (168 patients). With an AUC close to 0.98, Random Forest has the best ability to distinguish between different survival outcomes. With AUC values above 0.95, XGBoost and the PRIM framework exhibit performance patterns that are strikingly similar. Compared to the other algorithms, Logistic Regression exhibits greater separation while maintaining clinical performance that is acceptable. Good sensitivity at low false positive rates is indicated by the curves' steeper initial rise. All algorithms show good performance for the Mammographic masses dataset in Figure 8d. With an AUC above 0.97, XGBoost produces especially remarkable results, closely followed by Random Forest and the PRIM framework. The curves' outstanding early progression suggests that the models can maintain low false positive rates while achieving high true positive rates. For mammography screening applications, where false positives may result in needless procedures, this is essential. Finally, with AUC values above 0.96, Random Forest and XGBoost once more show superior performance for the relatively small but feature-rich NKI dataset in Figure 8e. In terms of discriminative ability, the PRIM framework maintains competitive performance, whereas Logistic Regression displays more noticeable disparities. The high AUC values indicate strong prognostic ability in spite of the dataset's difficulty, especially for the best-performing algorithms. Across all datasets, we observe several consistent patterns:  
* The best results are consistently obtained by Random Forest and XGBoost. * The PRIM framework maintains strong performance close to the best algorithms. * Despite its clinical viability, Logistic Regression usually exhibits a lower capacity for discrimination. * The models generally perform better in the high-sensitivity region, which is crucial for medical applications. * The performance remains robust across varying dataset sizes and feature dimensions.