Abstract
Abstract: In this research study, five machine learning algorithms—Support Vector Machine (SVM), Random Forest, Logistic Regression, Decision Tree (C4.5), and K-Nearest Neighbors (KNN)—were applied to the Breast Cancer Wisconsin Diagnostic dataset. The subsequent results underwent a thorough performance evaluation and comparison among these diverse classifiers. The primary objective was to predict and diagnose breast cancer using machine learning algorithms, determining the most effective approach based on factors such as the confusion matrix, accuracy, and precision. Notably, the findings highlight that the Support Vector Machine outperformed all other classifiers, achieving the highest accuracy at 97.2%.
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