## 5 Discussion  
This is the first study that has proven that dimensionality reduction approaches using FA and t-SNE may be used to identify between mild and severe subjects of breast cancer, as well as control women. Even though blood and saliva are not yet widely used as a source of samples for hormone analysis, they are a great research tool that provide a non-invasive and stress-free alternative to plasma and serum. This makes using them an appealing option. These samples have been demonstrated to be trustworthy and, in some situations, superior to other body fluids by displaying a very close association with the free testosterone levels in serum [45]. This has led to their recognition as extremely useful diagnostic tools. Saliva, on the other hand, has several benefits over blood as a sampling medium, including the fact that it may be easily collected by the participants themselves at regular intervals and that it does not require any specialized equipment for collection or storage [46; 47]. Hormones such as testosterone serum levels have a low level of reliability in the low ranges that are observed in normal women [48], and their levels can vary greatly depending on genetic, metabolic, and endocrine effects [49]. The concept that the measurements of free or bio-available testosterone are able to more precisely predict  
Figure 7: Confusion matrices for the DT, SVM, and KNN classifiers using t-SNE dimensionality reduction technique to demonstrate the saliva hormones performance from the normal subjects, benign patients, and malignant patients.  
androgenic effects than total testosterone levels has recently gained widespread acceptance. In order to ensure the reliability of our research, the 10 generated features corresponded to a total of 10 characteristics for each of the hormones that were analyzed. We made an effort to use all of the possible independent variable statistical features in order to obtain a broad and comprehensive perspective from the blood and saliva in order to classify the severity of BC into normal subjects, benign patients, and malignant cases. This was due to the co-linearity that may exist within this set of derived features, as well as the redundancy that arises from the utilization of the mean and the summation. On the other hand, the discovery that two or more independent variables were correlated leads one to infer that the shifts in one variable are related to changes in another variable. This can happen when two or more variables are found to be correlated. Because of these modifications, we were able to increase the size of the feature set, which was beneficial, given that we wanted to apply methods that reduced the dimensionality of the data. Because of this, the accuracy of the classifiers for the salivary bio-markers varied, which is why the results improved, especially with the DT, with an increase in classification accuracy from 66.67% to 93.3% and 90% when using t-SNE and FA, respectively. The accuracy for the blood biomarkers also improved in the results, particularly with the DT, which saw a boosted classification accuracy from 60% to 80% and 93.3% when utilizing FA and t-SNE, respectively, as statistical tools.