# Breast Cancer Classification Using an Adapted Bump-Hunting Algorithm
## 4 Results  
### Experimental Setup  
This section aims to present the setting for the experiment with details about the datasets and the materials used to compute the empirical evaluation.  
To evaluate the PRIM-based framework, we conducted a comparative study for the classifier from our PRIM-based framework against three classification algorithms for five real-life breast cancer datasets based on four measures to measure the performance of the classifiers namely: accuracy, precision, recall, and F1-score. We also provide the ROC and AUC for each dataset of the models to show model performance across all possible thresholds, making it particularly valuable for medical applications where the sensitivity-specificity trade-off may need adjustment.  
The five breast cancer datasets used can be displayed in Table 2. Wisconsin is taken from the UCI Machine Learning Repository while SEER and the ISPY1-clinica datasets are both taken from the National Cancer Institute of USA and they give as a target variable whether the patient is dead or alive according to some features. The NKI dataset was published by the Netherland Cancer Institute and has 1570 columns. Finally, the Mammographic masses dataset has been published by the Image Processing and Medical Engineering Department (BMT) at the Fraunhofer Institute for Integrated Circuits (IIS) in Germany. The datasets did not need preprocessing since PRIM can handle both categoric and numeric features, we therefore cleaned the missing values.  
For the four algorithms, we used a 10-fold cross-validation, and we relied on accuracy to choose, for each dataset, the best model. Therefore, the other measures allow us to mea  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Datasets** & **Nb of Instances** & **Nb of Attributes** & **Class Labels** & **Class Distribution** \\ \hline
**Wisconsin** & 569 & 32 & Malignant: 1 & 359 \\  & & & Benign: 0 & 210 \\
**SEER** & 4024 & 12 & Alive: 0 & 3408 \\  & & & Dead: 1 & 616 \\
**ISPY1-clinica** & 168 & 18 & No: not dead: 0 & 32 \\  & & & Yes: dead: 1 & 136 \\
**Mammographic masses** & 961 & 6 & 1: malignant & 445 \\
**NKI dataset** & 272 & 1570 & 1: dead & 195 \\  & & & 0: alive & 77 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Major properties of the datasets considered in the evaluation.  
sure the performance of our algorithm in classifying the different instances in comparison with the other methods.  
To generate XGboost, Random Forest, and Logistic Regression models, we worked in Python, with the framework Orange [38], setting the parameters as the best combination already selected by default. For the PRIM, we used the package \"prim\" in Python. The parameters of the PRIM were set according to the literature: the peeling criterion was 5%, the pasting criterion was 5%, and the support threshold was 30%.  
The imbalance situation in the SEER dataset and the ISPY1-clinica dataset call on the use of recall, precision, and F1-score. The recall is the proportion of the actual positive cases we can predict correctly with the model. It is a useful metric in medical cases where it does not matter if we raise a false alarm, but the actual positive cases should not go undetected. Precision is the proportion of the correctly predicted cases that actually turned out to be positive. It is useful if the false positives are a higher concern than the false negatives. In some cases, there is no clear distinction between whether precision is more important than recall, so we combine both of them and we create the F1-score. In practice, when we try to increase the precision, the recall goes down, and vice versa. The F1-score captures both trends in a single value. More explanations on the metrics in machine learning can be found in [40].