Patients at an early stage, which can help with prompt diagnosis and treatment of the condition. This study utilizes a retrospective cohort design, where health records of patients with and without breast cancer were obtained from a large healthcare database. The data were pre- processed to remove missing values, and feature engineering techniques were used to extract relevant features. The predictive models were created, QSVM, Classical ML models (SVM, Random Forest. The goal of this research was to develop a predictive model for detecting breast cancer early in patients. The models were able to achieve high accuracy, precision, and recall rates, indicating their effectiveness in predicting the likelihood of breast cancer in patients. The model can be used to pinpoint people at high risk for illness development who need additional diagnostic tests or early intervention. The creation of a Breast cancer predictive model using health information is the sole focus of this work. The model can be incorporated into clinical decision support systems. In order to examine the viability of adopting the model in clinical practice, more research is required to validate the model in a variety of demographics and situations.
Patients at an early stage, which can help with prompt diagnosis and treatment of the condition. This study utilizes a retrospective cohort design, where health records of patients with and without breast cancer were obtained from a large healthcare database. The data were pre- processed to remove missing values, and feature engineering techniques were used to extract relevant features. The predictive models were created, QSVM, Classical ML models (SVM, Random Forest. The goal of this research was to develop a predictive model for detecting breast cancer early in patients. The models were able to achieve high accuracy, precision, and recall rates, indicating their effectiveness in predicting the likelihood of breast cancer in patients. The model can be used to pinpoint people at high risk for illness development who need additional diagnostic tests or early intervention. The creation of a Breast cancer predictive model using health information is the sole focus of this work. The model can be incorporated into clinical decision support systems. In order to examine the viability of adopting the model in clinical practice, more research is required to validate the model in a variety of demographics and situations.
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.
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 a major health concern, and early detection can make a great difference in treatment and survival rates for breast cancer patients. Machine learning methods have noticeably improved prediction accuracy on high-dimensional medical datasets and have become widely used tools in medical diagnostics. The transition to a quantum computational framework has opened new directions in machine learning research. By using qubits instead of classical bits, quantum models may provide new ways to represent and process complex medical data. In medical diagnostics, this has led to a growing interest in whether quantum approaches can improve classification performance more effectively than classical methods. The purpose of this paper is to compare classical and quantum machine learning models on the task of breast cancer classification and to determine whether quantum models can achieve higher accuracy and faster prediction on a selected dataset. Alongside the main simulator-based experiment, a smaller experiment was performed on a real IBM quantum computer to demonstrate the practical execution of the same task under current hardware constraints. In the practical part of the study, the Wisconsin Diagnostic Breast Cancer (WDBC) dataset was used. This dataset consists of 569 samples with 30 numerical features extracted from digitized fine needle aspirate images and provides a reliable basis for evaluating model performance. A comparative analysis was conducted between classical and quantum machine learning models, including a support vector machine (SVM), an artificial neural network (ANN), a quantum support vector machine (QSVM), and a hybrid quantum-classical neural network (QNN). In the simulator-based experiment, both SVM and ANN achieved an accuracy of 0.956 with an F1-score of 0.965 on the test set, while QSVM reached an accuracy of 0.807 with an F1-score of 0.866 and the Hybrid QNN achieved 0.623 accuracy with an F1-score of 0.677. These quantum and hybrid models also required substantially longer training times than the classical baselines. A small hardware experiment on an IBM quantum device further illustrates both the practical feasibility and the current limitations of executing this classification task on noisy intermediate-scale quantum hardware.
Quantum computing technology allows for more efficient problem-solving and provides essentially distinct answers to computational challenges when compared to classical computations. Machine learning algorithms have come to the fore in medical research and offer promising avenues for increasing the accuracy of cancer diagnosis. Breast cancer represents the majority of cancer cases in women worldwide, making it a serious and widespread public health issue. Improving patient outcomes requires early identification of cancer. The proposed Quantum Enhanced Prediction (QEP) project delves into the predictive power of two distinct learning paradigms: Support Vector Machine and Quantum Support Vector Machine, using Wisconsin breast cancer dataset. The research underscores the complexity of algorithms in cancer prediction. A departure from the ZZ feature map in QSVM marked the beginning of an intentional shift from traditional design. However, the study looked at the integration of the Z element map and Pauli matrices, providing the foundation for improved accuracy and a better grasp of quantum mechanics in cancer prediction. This conceptual shift intends to investigate the possible influence of quantum computing on breast cancer prediction with more granularity.
Learning tasks are implemented via mappings of the sampled data set, including both the classical and the quantum framework. Biomedical data characterizing complex diseases such as cancer typically require an algorithmic support for clinical decisions, especially for early stage tumors that typify breast cancer patients, which are still controllable in a therapeutic and surgical way. Our case study consists of the prediction during the pre-operative stage of lymph node metastasis in breast cancer patients resulting in a negative diagnosis after clinical and radiological exams. The classifier adopted to establish a baseline is characterized by the result invariance for the order permutation of the input features, and it exploits stratifications in the training procedure. The quantum one mimics support vector machine mapping in a high-dimensional feature space, yielded by encoding into qubits, while being characterized by complexity. Feature selection is exploited to study the performances associated with a low number of features, thus implemented in a feasible time. Wide variations in sensitivity and specificity are observed in the selected optimal classifiers during cross-validations for both classification system types, with an easier detection of negative or positive cases depending on the choice between the two training schemes. Clinical practice is still far from being reached, even if the flexible structure of quantum-inspired classifier circuits guarantees further developments to rule interactions among features: this preliminary study is solely intended to provide an overview of the particular tree tensor network scheme in a simplified version adopting just product states, as well as to introduce typical machine learning procedures consisting of feature selection and classifier performance evaluation.
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.