<![CDATA[Breast cancer is one of the leading causes of death among women, especially if it is not detected early. Therefore, accurate and efficient classification methods are needed to aid the diagnosis process based on clinical data. This study aims to classify breast cancer using a hybrid approach that combines Deep Neural Networks (DNN) and Naive Bayes on the Wisconsin Breast Cancer Dataset (WBCD). DNN is used as a feature extraction method to generate a more informative feature representation from the original data, while Naive Bayes is used as the final classification algorithm. The dataset consists of 569 data points with 30 numerical features and two classes: benign and malignant. The research stages include data preprocessing, training the DNN using backpropagation, feature extraction from the hidden layer, and classification using Naive Bayes. The test results show that the model achieved an accuracy of 92.11%, precision of 92.31%, recall of 85.71%, and an F1-score of 88.89%. These results indicate that the proposed hybrid approach is capable of improving the quality of data representation and delivering fairly good classification performance. However, the lower recall value suggests that there are still some positive cases that have not been optimally detected.]]>
Copyrights © 2026
