Breast cancer, the unruly development of abnormal cells in breasts, is a pressing public health issue and needs early detection for improving survival rates. Machine learning algorithms have shown promising results in breast cancer classification and detection. This study evaluates the performances of traditional and quantum machine learning techniques for data classification of breast cancer, emphasizing their effectiveness in discerning patterns using reduced set of features. An approach is proposed to reduce 30-dimensional features to 2-dimensional features using widely applied Principal Component Analysis (PCA), Incremental PCA, Truncated Singular Value Decomposition and Kernel PCA techniques. This study aims to provide an insight on the impact of reduction of features on the classification of breast cancer using classical and quantum machine learning techniques.
Breast cancer, the unruly development of abnormal cells in breasts, is a pressing public health issue and needs early detection for improving survival rates. Machine learning algorithms have shown promising results in breast cancer classification and detection. This study evaluates the performances of traditional and quantum machine learning techniques for data classification of breast cancer, emphasizing their effectiveness in discerning patterns using reduced set of features. An approach is proposed to reduce 30-dimensional features to 2-dimensional features using widely applied Principal Component Analysis (PCA), Incremental PCA, Truncated Singular Value Decomposition and Kernel PCA techniques. This study aims to provide an insight on the impact of reduction of features on the classification of breast cancer using classical and quantum machine learning techniques.
Early and accurate diagnosis of breast cancer plays a critical role in improving patient survival rates and reducing treatment costs. Classical machine learning algorithms such as Logistic Regression and Support Vector Machines (SVM) have demonstrated strong performance in medical diagnosis tasks; however, their scalability and representational limits motivate the exploration of emerging paradigms. Quantum Machine Learning (QML), particularly Variational Quantum Classifiers (VQC), has recently gained attention due to its potential to model complex decision boundaries using quantum circuits. This paper presents a comparative analysis between a Variational Quantum Classifier and classical machine learning models for binary breast cancer classification. The study employs dimensionality-reduced clinical data and evaluates model performance using accuracy, confusion matrices, decision boundary visualization, Principal Component Analysis (PCA), and Receiver Operating Characteristic (ROC) curves. Experimental results indicate that the VQC achieves competitive performance with classical approaches, demonstrating its feasibility for small-scale medical datasets and highlighting the promise of quantum-enhanced learning in healthcare applications.
Abstract The breast cancer is widely prevalent, with millions of new cases and deaths reported annually. Globally, one in every 70 women is affected with breast cancer. It is apparent from the statistics that the number of breast cancer subjects are exponentially rising day by day. Early detection of the disease plays a crucial role to decrease the mortality rate and increase the survival chance. Quantum machine learning is an upcoming field in the era of big data, which helps to process the complex data more efficiently. This study aims to explore the QML models to accurately predict the breast cancer subjects and diagnose the disease in the early stage. Additionally, QML-based models aid in the classification of breast cancer as either benign or malignant. This study presents the comparative analysis of three QML models, namely QRF, QkNN and QSVM, for breast cancer classification using the Breast Cancer Wisconsin dataset. Each model is evaluated based on key performance metrics and the models achieved the accuracy of 97%, 94% and 90%, respectively. Hence, the results showed that the QRF outperformed QSVM and QkNN in classification accuracy, demonstrating superior ability to distinguish between malignant and benign tumors. This study highlights the potential of QML in medical diagnostics while emphasizing the need for further research in hybrid quantum-classical approaches, improved feature mapping techniques, and optimized quantum circuits.
Introduction: In recent years, high-dimensional clinical and genomic data have gained significant importance for prognosis and personalized medicine in breast cancer. But the use of quantum machine learning (QML) on such data is limited by the availability of few qubits, the computation time of quantum simulation, and dimensionality reduction. This work systematically compares several QML architectures for breast cancer classification in the presence of realistic and simulator constraints. Methods: The experiments were performed on a dataset of METABRIC breast cancer patients (2,509 patients). After handling missing values and one-hot encoding, there were 63 features in the processed data set. The feature space was reduced by Principal Component Analysis (PCA) to 12, 4 and 2 components for the implementations of quantum computers, respectively, with 56.10 ± 0.14%, 26.78 ± 0.57% and 16.23 ± 0.33% of the variance retained. Three QML models were tested: Quantum Neural Networks (QNN), Quantum K-Nearest Neighbors (QKNN), and Quantum Support Vector Machines (QSVM), with the models being simulated. Seven classical classification models were tested: Logistic Regression, SVM with RBF kernel, K-Nearest Neighbors, Random Forest, XGBoost, LightGBM and Multilayer Perceptron, both with PCA-matched and full 63-feature representation. All primary results are reported with 5-fold cross validation. Results: Among the evaluated QML architectures, QKNN using 12 principal components achieved the strongest performance, attaining an accuracy of **75.11% ± 3.76%**, an F1-score of **0.7088 ± 0.0433**, and a ROC-AUC of **0.8148 ± 0.0394**. Even though all of the QML models performed significantly poorly in comparison to classical models trained on the entire 63-feature data set, the latter models were able to achieve about **94% accuracy** with XGBoost, Random Forest, and Logistic Regression. The comparison also showed the effect of information loss due to PCA is significant in predictive performance in both classical and quantum models. Discussion: The results show that for high dimensional breast cancer data, currently available simulator-based QML models can learn meaningful patterns with limited quantum resources, but are not as effective as powerful classical machine learning models when complete feature representations are available. The study does not report any sort of quantum advantage or clinical use, but rather a benchmark of current QML architectures that has been rigorously calculated and repeated, with a focus on the impact of dimensionality reduction, validation approaches, and simulator limitations, as well as outlining challenges that need to be overcome prior to practical implementation on real quantum hardware.
Breast cancer is rated as one of the top leading diagnosed and deadliest cancers affecting women worldwide and causing high mortality rate among cancers. With the data-driven applications, hybrid quantum-classical machine learning models have evolved as significant alternatives for handling complex feature spaces with the growing interest in quantum computing. This paper presents a systematic comparative study of classical and quantum machine learning approaches for breast cancer classification using the Wisconsin Breast Cancer Dataset. In this architecture we have preprocessed classical feature vectors using standard normalized techniques and parallelly quantum states are mapped through data embedding techniques which relies on parameterized rotational gates. In this work we have implemented all quantum models using the Pennylane library, resulting in hybrid quantum–classical optimization on near-term quantum simulators. The comparison results in multiple classical and quantum models. In classical we have Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Convolutional Neural Networks (CNN), which are compared with the quantum-based models such as the Variational Quantum Classifier (VQC), Quantum Support Vector Machine (QSVM) and Quantum Convolutional Neural Network (QCNN). In QSVM, we have performed dimensionality reduction using Principal Component Analysis (PCA) before to quantum embedding, and quantum-generated features are used to train a classical linear SVM. The evaluation metrics results in accuracy, precision, recall, F1-score, ROC curves and confusion matrices. The classical models achieves the accuracy in the range of 94%-96%, whereas competitive performance of quantum models demonstrate depending on the embedding strategy and circuit configuration. In the implemented quantum models, the variational quantum classifier achieved the highest classification accuracy of 96.06%, surpassing other evaluated models. These results demonstrate quantum embedding combined with variational quantum circuits can effectively capture discriminative patterns in medical datasets, indicating the implementation of quantum machine learning models for breast cancer diagnosis with the present quantum simulating platforms.
The application of machine learning has shown significant progress in various fields, including clinical data classification. This study presents a comparative analysis between the classical Support Vector Machine (SVM) and Quantum Support Vector Machine (QSVM) for breast cancer classification using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Principal Component Analysis (PCA) was applied as dimension reduction strategy across three configurations (PCA = 2, PCA = 4, and PCA = 6) to evaluate the sensitivity of both models to the feature space dimension. Preprocessing included log1p transformation on 22 skewed features, Manual SMOTE balancing, and MinMaxScaler normalization. SVM was optimized via GridSearchCV (80 parameter combinations, cv = 5), while QSVM employed a ZZFeatureMap quantum kernel (reps = 2, entanglement = full) on qasm_simulator. Experimental results demonstrate that SVM achieved superior accuracy across all configurations, with peak performance at PCA = 6 (Accuracy= 98.83%, AUC = 0.9985, Specificity = 1.0000, Sensitivity = 0.9688, Precision = 1.0000, NPV = 0.9817, and F1-score for malignant = 0.98). QSVM achieved its highest accuracy at PCA = 4 (92.98%), AUC = 0.9622, Sensitivity = 0.9219, Precision = 0.8939, NPV = 0.9524, and F1-score for malignant = 0.91. Across the three PCA configurations, QSVM exhibited a non-monotonic relationship between PCA dimensions and quantum kernel performance . These findings provide a quantitative benchmark for comparing quantum and classical machine learning in breast cancer diagnosis.
Breast cancer continues to be among the most common causes of cancer death globally, with early and precise diagnosis playing a pivotal role in enhancing patient survival. Traditional machine learning (ML) techniques have shown great promise in the field of medical imaging and diagnosis; however, the emergence of quantum machine learning (QML) offers new avenues for the improvement of pattern discovery and diagnostic accuracy in high-dimensional medical data. This work describes a thorough benchmark comparison of quantum and classical machine learning methods for breast cancer diagnosis over the Wisconsin Diagnostic Breast Cancer data and mammographic image data. We compare variational quantum classifiers (VQC), quantum kernel methods (QKM), quantum convolutional neural networks (QCNNs), and hybrid quantum-classical neural structures with traditional classical baselines like support vector machines (SVM) and convolutional neural networks (CNN). QKM techniques perform better in high-dimensional feature spaces and better generalize on external validation sets. This implementation framework describes in depth how to develop, train, and deploy QML models in the clinical workflow, including optimization approaches, code structures, and deployment issues. The work demonstrates that it is possible to streamline breast cancer screening and diagnosis using QML with more accurate and more efficient solutions, which would lead to a huge impact on patient outcomes.