Effective Breast Cancer Classification Using Deep MLP, Feature-Fused Autoencoder and Weight-Tuned Decision Tree

Breast cancer remains a leading cause of death among women worldwide, underscoring the urgent need for practical diagnostic tools. This paper presents an advanced machine learning algorithm designed to improve classification accuracy in breast cancer diagnosis. 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 via cross-validation and square weight adjustment. The proposed method was rigorously tested using the Wisconsin breast cancer dataset, employing k-fold cross-validation to ensure robustness and generalizability. Key performance indicators, including accuracy, precision, recall, F1-score, and area under the curve (AUC), were used to evaluate the model’s ability to distinguish between malignant and benign tumors. Our results suggest that this combination model outperforms traditional classification methods, with high accuracy and robust performance across data partitions. The main contribution of this research is the development of a new framework for deep learning. Auto-encoder and decision tree results show that this system has strong potential to improve breast cancer diagnosis, offering physicians a reliable and effective tool.