## 3 Materials and Methods
### Classification Stage  
It was determined through an exhaustive study of the BC data that the individuals could be divided into three categories: normal, benign, and malignant. The quality of the generated features had a significant influence on the level of accuracy achieved by the classifier. As a consequence of this, the accuracy of the results of the classification can be affected not only by the techniques for dimensionality reduction that are selected, but also by the type of classifier that is utilized. In order to investigate BC differences based on emotional states, three classifiers were utilized: DT, SVM, and KNN. The DT has a structure similar to a flowchart and is based on a set of rules; therefore, it is recognized to be very effective and provides clear guidance. This method begins at the very top with a question, which is also referred to as a \"root node\". The algorithm works its way through the tree branches in a certain order, with each branch being determined by the responses to the questions that came before it until it reaches the leaf node, at which point it would indicate whether or not the individual is part of three leaf nodes that were malignant, benign, or part of the normal group [34]. DT is a supervised learning algorithm that needs labeled data to be trained. Labeled data would include patients with known diagnoses for normal, benign, or malignant classification. The DT approach is effective for classification. To avoid overfitting, pick parameters carefully. Additionally, the training set should be reflective of the data that the tree will classify. The Gini impurity and entropy are the main splitting criteria. The minimal number of samples in a leaf node before the tree can split is 50. This and the tree's maximum depth avoid overfitting. Additionally, the DT classification tree model was applied here as well. It does this by using an algorithm for recursive partitioning, which produces nodes based on certain criteria for splitting the data. After the nodes have been produced and divided, they are used to grow a tree. In order to make use of the split criteria, it is necessary to locate the ideal split point. A function that is derived from the variance function is used to evaluate the splitting criteria's overall level of quality. A function is used to analyze each possible split point, beginning with binary splits, and these determine which one provides the best results when compared to an optimization criterion. This allows the optimal point for splitting to be determined. In this particular body of work, Gini's diversity index has served a role as an optimization criterion. The process of partitioning the instance space comes to an end when the classification tree reaches the pure node; a node is considered pure if it only contains observations that belong to a single class. The number 50 was used as the parameter in the DT method for classifying patients as normal, benign, or malignant [34]. For the SVM classifier, the optimizing C on the training set was selected to acquire the best results. To be more specific, the SVMs were trained for a variety of C values that fell within the range of \(-4<\log_{10}(C)<4\) in C values ranging from 0.0001, 0.001, 0.01, 0.1, 0, 10, 100, 1000, 10000. During the testing method, the result obtained for \(C=10\) was the most favorable. For the purpose of implementing the multi-class SVMs classifier, the RBF-kernel functions were put to use. During the SVM training, the smoothing parameter was chosen based on the lowest mis-classification rate that was obtained from the training dataset. This was done so that the best possible model could be produced. The best value of \(\sigma\) can only be established by methodically experimenting with different values of \(\sigma\) during the various training sessions. As a result, the value of \(\sigma\) was changed between 0.1 and 1, with a step size of 0.1 between each change. At the value of \(\sigma=0.5\), the rate of mis-classification was reduced to its lowest possible value.