## 2 Related Works  
Many approaches have been proposed for diagnosing BC, but they all suffer from significant shortcomings when it comes to spotting the disease at an early stage [14]. Therefore, the job of clinical doctors is made more difficult by the fact that there are multiple factors to consider when making a diagnosis of BC. Diagnosing BC relies heavily on medical records and a system of expert judgment. All of these earlier proposals have aimed to diagnose BC in various ways. The downside to all of these methods is that they take longer to execute and have sub-par prediction accuracy. Better early detection is essential for effective treatment and a full recovery from BC, but the BC identification method's prediction accuracy still needs work [5]. Low accuracy and lengthy computation times are thus major issues with the current approaches; these may be attributable to the inclusion of inappropriate features in the data. This calls for novel methods of BC detection to be implemented. It is a significant challenge and research gap to enhance the ML model's prediction accuracy [5]. The dataset used is based on data from the University of Wisconsin's database, which has seen extensive application in BC detection. To determine whether a tumor was benign or malignant in BC, [PERSON] et al. utilized a naive Bayes classifier, which had an accuracy of 85% [19]. Breast cancer prediction accuracy has also been measured using the naive Bayes, C4.5 decision tree, and SVM classification algorithms by [PERSON] et al. There were a total of 35 data attributes that were gathered. By eliminating less important features, an effective feature selection method can boost a model's accuracy [20]. In addition, using data on BC obtained from the Leiden University Medical Center, [PERSON] et al. constructed a decision tree algorithm. The classifier was accurate for 70% of the time [21]. Lately, hormone tests using blood and saliva have emerged as a viable method in the detection of BC. Estrogen and progesterone are two hormones that play a significant role in the onset and progression of BC [22]. Increased levels of these hormones may be an early warning sign of sickness. Hormone analysis from saliva is a quick and easy way to check your hormone levels without any discomfort. Researchers can test for hormonal imbalances that may be related to BC by analyzing the samples of a patient's saliva. Because it allows for routine screening and early intervention, this method may improve treatment outcomes for those at a higher risk of developing BC [23]. New techniques are constantly being developed, and the profession as a whole is expanding at a dizzying rate. In the future, we can anticipate even more precise and trustworthy approaches for breast cancer detection. Using a collection of clinical variables, [PERSON] et al. [24] presented a data mining method for predicting breast cancer. The authors evaluated and contrasted the efficacy of four distinct data mining algorithms: the support vector machine (SVM), the artificial neural network (ANN), the naive Bayes classifier, and the AdaBoost tree. The SVM method was the most effective, with a success rate of 96.2%. [PERSON] et al. [25] have employed three data mining methods, namely, k-nearest neighbours (KNN), the support vector machine (SVM), and random forest (RF),to predict breast cancer. The three algorithms were evaluated and compared using data from the Wisconsin Breast Cancer dataset. The RF algorithm was determined to have the highest accuracy (97.1%). Breast cancer recurrence has been analyzed using a data mining method that was proposed by [PERSON] et al. [26]. The authors implemented the decision tree, naive Bayes, and support vector machine data mining techniques on the Wisconsin Breast Cancer dataset. They observed that a 96.1% accuracy could be attained using the decision tree approach. Six data mining methods for detecting breast cancer were evaluated by [PERSON] et al. [27].