Objective: The objective of this study is to classify breast cancer using the Bagged Classification and Regression Trees (CART) method and identify possible risk factors that play a role in the development of the disease. By evaluating the classification performance of the model, this study aims to contribute to the development of early detection and preventive strategies. Materials and Methods: The study used an open-access dataset that includes variables such as menopausal status, tumor grade, lymph nodes, progesterone and estrogen receptors, age, and tumor size. The dataset was divided into training (80%) and testing (20%) subsets, and a 10-fold cross-validation approach was used to improve generalization. Bagged CART was employed for classification, and model performance was evaluated using metrics like accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. Results: The Bagged CART model achieved high classification performance, with an accuracy of 99.6%, sensitivity of 100%, and specificity of 99.2%. Variable importance analysis revealed that recurrence-free survival time was the most critical factor in the classification process, followed by progesterone and estrogen receptors, lymph nodes, tumor size, and age. Variables such as tumor grade and hormonal treatment status had lower importance. Conclusion: The findings indicate that Bagged CART is highly effective in breast cancer classification, with a strong emphasis on factors such as recurrence-free survival time, hormone receptor status, and tumor characteristics. The model's high accuracy and reliability suggest that it could contribute to enhancing early detection and preventive strategies, ultimately improving patient outcome.