###### Abstract  
Countless women and men worldwide have lost their lives to breast cancer (BC). Although researchers from around the world have proposed various diagnostic methods for detecting this disease, there is still room for improvement in the accuracy and efficiency with which they can be used. A novel approach has been proposed for the early detection of BC by applying data mining techniques to the levels of prolactin (P), testosterone (T), cortisol (C), and human chorionic gonadotropin (HCG) in the blood and saliva of 20 women with histologically confirmed BC, 20 benign subjects, and 20 age-matched control women. In the proposed method, blood and saliva were used to categorize the severity of the BC into normal, benign, and malignant cases. Ten statistical features were collected to identify the severity of the BC using three different classification schemes--a decision tree (DT), a support vector machine (SVM), and k-nearest neighbors (KNN) were evaluated. Moreover, dimensionality reduction techniques using factor analysis (FA) and t-stochastic neighbor embedding (t-SNE) have been computed to obtain the best hyperparameters. The model has been validated using the k-fold cross-validation method in the proposed approach. Metrics for gauging a model's effectiveness were applied. Dimensionality reduction approaches for salivary biomarkers enhanced the results, particularly with the DT, thereby increasing the classification accuracy from 66.67% to 93.3% and 90%, respectively, by utilizing t-SNE and FA. Furthermore, dimensionality reduction strategies for blood biomarkers enhanced the results, particularly with the DT, thereby increasing the classification accuracy from 60% to 80% and 93.3%, respectively, by utilizing FA and t-SNE. These findings point to t-SNE as a potentially useful feature selection for aiding in the identification of patients with BC, as it consistently improves the discrimination of benign, malignant, and control healthy subjects, thereby promising to aid in the improvement of breast tumour early detection. breast cancer; machine learning; support vector machine; k-nearest neighbors accuracy; decision tree; statistical features; dimensionality reduction; blood and saliva hormones +
Footnote †: journal: Medical  
effective prognosis and patient survival rates for BC over the past few decades [2; 3]. Tumors are collections of dividing cells that grow to become masses of extra tissue; they can be malignant (cancerous) or benign. Breast tissue biopsies use a highly accurate method for detecting cancer [4]. Unfortunately, the biopsy procedure itself can be quite uncomfortable for the patients [5]. There has been a substantial development of technologies for BC detection throughout the years, including the use of image processing and analysis [5]. These cutting-edge methods allow doctors to better diagnose patients and provide better care. Radiologists can use images to detect irregularities and possible malignant tumors in the early stages of BC. Common methods for seeing breast tissue and detecting abnormalities include mammography, ultrasound, and magnetic resonance imaging (MRI) [6]. The mammogram is the method of choice for detecting BC [6]; this technique uses a two-dimensional (2-D) projection image of the breast for diagnosis. Details that are too small to be seen with the naked eye can be captured by these photos [7]. The use of images obtained from mammography and MRI for the purpose of diagnosing breast cancer offers a number of obstacles [5]. Although these imaging methods are essential for early diagnosis, they also come with a variety of drawbacks, such as the fact that mammography, despite its widespread application, may have limitations in terms of its sensitivity [8], particularly in women who have breast tissue that is dense. The MRI has a high sensitivity, but due to the fact that it is particularly sensitive to benign lesions, it can also lead to false positives [8].