## 4 Results  
Learning patterns from data, feature extraction, and dimensionality reduction let a model or classifier make judgements, especially in complicated tasks such as BC categorization. This study trained DT, SVM, and KNN models on labeled data. The models learned feature-label patterns during training. The model performance was robustly performed via cross-validation. Finally, the model was evaluated on a new dataset to determine its real-world performance. This stage determines if the model overfit (learned noise in the training data) or underfit (failed to capture patterns). Overfitting can be prevented by splitting the dataset into several smaller sub-sets (folds)--keeping one fold aside for testing purposes while training the model on the remaining four folds. These procedures were performed five times, and the model's efficacy on the unseen fold was assessed using the selected metric (precision, sensitivity, specificity, accuracy, and F1-score). Finally, a single performance score for the model was calculated by averaging the performance of the used metrics throughout all five iterations. The authors of t-SNE have conceded that, depending on the starting point and other random factors, the algorithm may return different results despite using the same data and parameters. Thus, we have used a fixed random seed to guarantee that the process always began in the same place. Thirty iterations were performed. As a result, the algorithm should eventually reach a more robust answer. To solve the reproducibility problem of t-SNE, it is recommended to run the algorithm five times with different random seedsand to then take an average of the results so that the classification results are not skewed due to overfitting. In order to further analyze the data, it was split into five independent datasets of similar sizes. We used one of these sub-sets as our test data, and we used the other nine to train our classifier. Five iterations of this process yielded ten reliable results. The accuracy of this dataset's 5-fold CV was determined by averaging these results. This intended to lessen the effect of chance and to lead to more consistent outcomes. The features were first calculated using the training set to create the input features vector for t-SNE. After that, dimensionality reduction was accomplished by computing both the training and testing sets. The effectiveness of the blood hormones in normal, benign, and malignant subjects was demonstrated through the use of performance assessment criteria such as precision, sensitivity, specificity, accuracy, and F1-score for the DT, SVM, and KNN classifiers, as is shown in Table 2.  
Figure 2 displays the confusion matrix for normal subjects', benign patients', and malignant patients' identification from the blood levels of P, T, C, and HCG using statistical features and KNN, SVM, and DT classifiers, respectively. The correct recognition is observed on the diagonal, whereas the off-diagonal represents the substitution errors. The percentage of correctly classified data from the DT, SVM, and KNN classifiers utilizing the blood level hormones is shown in Figure 2. The DT's accuracy was 60%, the SVM's was 56.67%, and the KNN's was 53.33%. Hence, using the DT, six of the control patients, five of the benign patients, and seven of the malignant patients were properly identified, whereas three of the malignant were incorrectly classified as one benign patient and two control healthy subjects. Moreover, five of the benign patients were incorrectly classified as malignant patients, and four of the control subjects were incorrectly classified as malignant patients.