Abstract: The number of fatalities from breast cancer is rising dramatically every year. It is the most common kind of cancer overall and the leading cause of death for women globally. Any advancement in the identification and prognosis of cancer is crucial to a long and healthy life. Therefore, it's critical to have a high level of accuracy in cancer prognosis in order to update patient survival standards and treatment aspects. Machine learning approaches have shown to be a powerful method, have become a research hotspot, and may significantly contribute to the process of early detection and prediction of breast cancer. Using the Breast Cancer Wisconsin Diagnostic dataset, we ran five machine learning algorithms through this study: Support Vector Machine (SVM), Classification and Regression Tree (CART), Navi Bayes, and K-Nearest Neighbors (KNN). Once the results were in, we compared and evaluated the performance of each classifier. This study paper's primary goal is to identify the most efficient machine-learning algorithms in terms of confusion matrix, accuracy, and precision for the detection and prediction of breast cancer. The Support Vector Machine is shown to have attained the maximum accuracy of 97.2%, outperforming all other classifiers. All of the work is completed in the Anaconda environment using the Scikit-learn package and the Python programming language.

| DOI: 10.17148/IARJSET.2024.11723