# Breast Cancer Classification Using an Adapted Bump-Hunting Algorithm
## 2 Material and Methods
### State of the Art of Breast Cancer Classification  
Breast cancer is very popular among cancer predictions in machine learning. Several researchers have developed and implemented techniques over the years to look for new patient profiles or feature combinations that can lead medical teams to better breast cancer detection [11]. In this section, we present several relevant papers related to using machine learning algorithms to predict breast cancer to determine the algorithms to adopt for our empirical study. More detailed reviews can be found in [12; 13; 14]. The authors in [15] have investigated breast cancer in Chinese women by looking at the signs before the symptoms are even shown. It has been shown that it is as successful as the accuracy of the prediction model used for the screening. The article evaluates and compares the performance of four ML algorithms, XGBoost, Random Forest, Deep Neural Network, and Logistic Regression, on predicting breast cancer. The model training was performed using a dataset consisting of 7127 breast cancer cases and 7127 matched healthy controls, while the performance was measured based on the AUC, sensitivity, specificity, and accuracy following a repeated 5-fold cross-validation procedure. The results showed that all three novel ML algorithms outperformed Logistic Regression in terms of discriminatory accuracy in identifying women at high risk of cancer. XGBoost also proved to be the best to develop a breast cancer model using breast cancer risk factors. Five ML algorithms were applied to the Breast Cancer Wisconsin Diagnostic dataset in [16] to determine which are the best and most effective in terms of accuracy, confusion matrix, AUC, sensitivity, and precision to predict and diagnose breast cancer. The algorithms used for the study are SVM, Random Forest, Logistic Regression, Decision Tree, and K-NN. Comparing the results showed that SVM scored a higher accuracy of 97.2%, with a precision of 97.5% and AUC of 96.6%, and outperformed the rest of the algorithms. The authors in [17] conducted a study to evaluate and compare 8 ML algorithms: Gaussian Naive Bayes (GNB), K-Nearest Neighbors (K-NN), SVM, RF, AdaBoost, Gradient Boosting (GB), XGBoost, and Multi-Layer Perceptron (MLP). The algorithms were applied to the Breast Cancer Wisconsin dataset, using 5-fold cross-validation. The XGBoost results showed great performance and accuracy, concluding that this algorithm is the best to predict breast cancer in the Wisconsin dataset, with an accuracy of 97.1%, recall of 96.75%, a precision of 97.28%, an F1-score of 96.99%, and an AUC of 99.61%. Three machine learning techniques for predicting breast cancer were used in [18]. The article aims to develop predictive models for breast cancer recurrence in patients who followed up for two years by applying data mining techniques. The main goal was to compare the performance through the sensitivity, specificity, and accuracy of well-known ML algorithms: the Decision Tree, Support Vector Machine, and Artificial Neural Network algorithms. The study was applied to a dataset of 1189 records, 22 predictor variables, and 1 outcome variable using 10-fold cross-validation for measuring the unbiased prediction accuracy. The results showed that the SVM classification model predicted breast cancer with the lowest error rate and the highest accuracy of 0.957, while the DT model was the lowest of all in terms of accuracy with 0.936, and ANN scored 0.947. To predict breast cancer metastasis in [19], the researchers used 4 ML algorithms, RF, SVM, LR, and Bayesian classification, to evaluate serum human epidermal growth factor receptor 2 (sHER2) as part of a combination of clinicopathological features used to predict breast cancer metastasis. These algorithms were applied to a sample cohort that comprised 302 patients. The results showed that the Random Forest-based model outperformed the rest of the algorithms, with an AUC value of 0.75 (\(p<0.001\)), an accuracy of 0.75, a sensitivity of 0.80, and a specificity of 0.71.