<![CDATA[Breast cancer is a disease with a high mortality rate in women worldwide, making early detection a crucial factor in increasing the chances of successful treatment and patient survival. Advances in computing technology, particularly machine learning, have created opportunities to use medical data to inform decision-making in the diagnostic process. This study aims to analyze and compare the performance of the Decision Tree and Random Forest algorithms in classifying breast cancer using the Wisconsin Breast Cancer Dataset. The dataset comprises 569 data points with 30 numeric attributes representing cancer cell characteristics and a class label indicating benign or malignant cancer. The research stages include data preprocessing, splitting the data into training and test sets (80:20), applying the classification algorithm, and evaluating model performance using accuracy, precision, recall, and F1-score metrics. The test results show that the Random Forest algorithm outperforms the Decision Tree. Random Forest achieved an accuracy of 98.68% on the training data and 95.61% on the test data, while Decision Tree achieved an accuracy of 96.92% on the training data and 91.23% on the test data. This difference indicates that Random Forest has better generalization capabilities and is more resistant to overfitting. The findings of this study indicate that Random Forest is more effective for data-based breast cancer classification than Decision Tree. Therefore, the Random Forest algorithm is recommended as a more reliable method to support decision support systems in early breast cancer detection.]]>
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