###### Abstract
Both mammography and MRI have the potential to provide false positive results, which can result in patients undergoing needless biopsies and experiencing emotional discomfort [9]. An overdiagnosis, in which benign tumours are identified and treated as dangerous, might result in overtreatment [9]. In addition, highly trained radiologists are required to appropriately interpret the results of mammograms and MRIs [10]. To prevent errors in diagnosis as much as possible, training and experience are absolutely necessary. In addition, cost and accessibility are important considerations, because an MRI scan can be rather expensive and may not be readily available to all individuals [8]. Because of this, its extensive usage in routine screening is restricted. In terms of data variability, the variability in image quality, location, and patient characteristics might affect the accuracy of both MRI-based detection methods and mammography-based detection methods [10; 11; 12]. In addition, image analysis tools have helped radiologists in image interpretation and BC detection, as these systems can be more successful and trustworthy in their diagnoses, because they make use of artificial intelligence to increase the accuracy of BC detection [5]. However, there are computational challenges to consider: the use of artificial intelligence for the analysis of the mammography and MRI images necessitate the collection of vast datasets for the purpose of training robust models; in addition, there is a concern regarding the models' reliability and generalization [13]. 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. Therefore, the development of BC diagnostic methods that allow for early detection of the disease and that prompt treatment and recovery is crucial. In this study, we propose a machine-learning-based method for early BC detection [14]. Suitable features have been chosen in the proposed method by employing two-dimensionality reduction techniques such as factor analysis (FA) and t-stochastic neighbor embedding (t-SNE). To train and test their algorithms, the machine learning systems needed the right kind of data. By using a dataset with a good distribution for both training and testing, the models can improve their overall efficiency. The use of suitable features from the data can also improve the classification model's performance. Therefore, improving model performance requires careful attention to data structure and feature selection. There has been some success in using the specific classifiers that are typically employed for classification-related problems to differentiate between people with BC and healthy individuals [5; 7]. In this paper, the decision tree (DT), support vector machine (SVM) and k-nearest neighbors (KNN) modelas were evaluated using the collected statistical features, with FA and t-SNE serving as dimensionality reduction techniques methods. The Elwiya Oncology Teaching Hospital and the Center for Early BC Detection have generously donated their BC datasets for use in evaluating the effectiveness of the proposed system.