###### Abstract
Both the proposed method and the performance evaluation metrics have been validated via the k-fold cross-validation method. The following are the most significant additions made by this work. Supervised learning has been used to examine significant hormones in theblood and saliva for accurate BC diagnosis. We found that combining blood biomarkers with the DT was an effective method for identifying BC. Because blood and saliva hormones have the potential to serve as non-invasive and easily accessible biomarkers, using them in diagnostic laboratory testing can provide useful information regarding the diagnosis of breast cancer. Blood testing makes early diagnosis possible, which is helpful for people who are at higher risk. Biomarkers that can be found in saliva provide a patient-friendly way of collecting specimens, which enables the detection of cancer signals. Through the use of blood or saliva samples, laboratory tests determine hereditary risks, which in turn indicate hormone receptor status, which in turn influences treatment selections. This novel technique improves the ability to diagnose breast cancer in its earlier stages. Researchers choose drugs by monitoring changes in blood and plasma proteins, which can be found in the body. Methods for detecting breast cancer that are not intrusive and focused on the patient, such as using blood and saliva hormones, have been shown to be effective. Dimensionality reduction using the t-SNE method was also used in this investigation. FA and t-SNE were used in this study for the first time to differentiate between mild and severe cases of BC. The purpose of using t-SNE is to preserve the local associations between points, which appear intuitively to be clustering rather than unrolling [15]. This is the motivation behind the usage of t-SNE; it is a non-linear dimensionality reduction strategy that focuses on keeping the structure of neighboring points [16]. It provides somewhat different outcomes each time on the same dataset, but these results are all focused on retaining the same information. The t-SNE approach is a powerful dimensionality reduction technique that is used in data analysis to convert the high-dimensional Euclidean distances between data points into conditional probabilities that represent similarities [15]. Despite its stochastic nature, for several reasons, such as the preservation of local structures, the t-SNE is particularly effective at preserving local structures in high-dimensional data, as it focuses on maintaining the similarity relationships between data points, thereby making it useful for visualizing clusters and patterns that might be lost in other dimensionality reduction techniques such as principal component analysis (PCA) [17]. Moreover, t-SNE produces visually appealing embeddings that can help analysts and researchers interpret complex data [18]. Furthermore, t-SNE complements other dimensionality reduction methods such as PCA; it can be used to gain a more comprehensive understanding of the data [16]. Therefore, t-SNE's ability to reveal local structures encourages researchers and analysts to often employ t-SNE alongside other techniques to uncover hidden patterns and gain a richer understanding of datasets. Cancer is just one example of a disease where the symptoms do not appear until the disease has progressed to a later stage, at which point it is usually too late to treat effectively, thereby making early diagnosis crucial. Laboratory-based methods, such as blood and saliva tests with machine learning (ML) methods, are highly suitable for the detection of BC, because they circumvent the difficulties of invasive-based methods. Since blood tests are now often used to diagnose a variety of minor conditions, it would be great to be able to utilize them to diagnose major diseases such as cancer. Therefore, proper treatment and a full recovery depend on detecting BC at an early stage.