A Quantum-Enhanced Model for Accurate Breast Cancer Classification and Early Diagnosis

Accurate and timely diagnosis of breast cancer greatly improves treatment effectiveness and patient survival. In this work, we have suggested a quantum-classical framework for breast cancer classification. The pipeline starts with Principal Component Analysis (PCA) to preserve 90-95% of the dataset variance. Then feature selection is done with Random Forest and Recursive Feature Elimination, retaining the top 10 strongest features. The features are normalized and fed to a Variational Quantum Circuit (VQC) Classifier built with the Pennylane library. The VQC employs a parameterized quantum circuit (PQC) with multiple rotation gates and entanglement layers and is run over a simulator. The experimental results demonstrate that the VQC attained a classification test accuracy of 96.49% and AUC-ROC of 0.9985 over the test set. The model has produced robust generalization performance, shown by its excellent performance over unknown data. Results indicate that the VQC competes with the benchmarked classical models and has potential benefits in terms of applicability and scalability to healthcare applications in terms of accuracy. This work emphasizes the synergy of classical preprocessing and quantum classification in designing efficient and interpretable healthcare applications.