Breast cancer is a prevalent and potentially life-threatening disease that requires accurate and early detection for effective treatment. In this article, we suggest an ensemble learning strategy for feature extraction based on wavelet transforms to enhance the classification accuracy of breast cancer. The objective of this research is to enhance the predictive accuracy of the model by incorporating wavelet-based feature extraction into the ensemble learning framework. We employ the breast cancer dataset from the UCI repository, consisting of various attributes related to breast cancer. The proposed method utilizes wavelet transforms, specifically the Daubechies 1 (db1) wavelet, with 5 levels of decomposition. Statistical features are extracted from the approximation and detailed coefficients obtained through the wavelet transform.