Breast cancer classification using effective machine learning techniques

Breast cancer remains a leading cause of death among women worldwide, underscoring the urgent need for practical diagnostic tools. This work presents an advanced machine learning algorithm designed to enhance the classification accuracy of breast cancer. The system integrates a deep multi-layer perceptron (Deep MLP) for feature extraction, a feature-fused autoencoder for efficient dimensional reduction, and a weight-tuned decision-tree classifier optimized by cross-validation and square weight adjustment. The Wisconsin breast cancer dataset is utilized to test the results of the method rigorously using k-fold cross-validation. Optimizing performance has been done under different hyperparameters, namely, the number of hidden units, dropout rate, batch size, as well as test-train percentages. The performance of the model was evaluated under all the given conditions for key Metrics. These are the Accuracy, Precision, Recall, F1-score, and area under the curve (AUC). Using the above metrics, the model was able to distinguish malignant and benign tumors. Our findings show that this approach performs better than traditional classification methods, leading to accurate and robust results across several data partitions. This research contributes to a new framework pertaining to deep learning, Auto-encoder, and decision tree, which clearly shows that this framework has a very high probability of impacting breast cancer diagnosis while providing usefulness to physicians. Furthermore, the use of the METABRIC dataset ,which is substantially larger and more diverse than classical breast cancer datasets ,provided a robust evaluation environment for the proposed hybrid model. Its rich clinical and genomic features enabled deeper validation of the model's generalization capability and demonstrated its effectiveness in handling complex, real-world data.