Abstract The medical information system is rich in datasets, but no intelligent systems can easily analyse the disease. Recently, ML (Machine Learning)‐based algorithms have acted as a handy diagnostic tool to identify whether a person is affected by thyroid or not. However, they produced classification with low accuracy and led to misclassification. Hence, the proposed system combines quantum computing with ML techniques to enhance computational power and precision. The system employs modified QPSO (Quantum Particle Swarm Optimisation) for feature selection since its searching performance is better than that of conventional PSO for selecting the optimum global position of the particle, thus selecting the relevant feature. Whereas, the QSVM (Quantum Support Vector Machine) is implemented for more accurate classification than classical SVM, as it tends to capture complex patterns in data produced due to high dimensional feature space applied by quantum kernel functions. This combination of modified QPSO and QSVM tends to increase the performance accuracy significantly. The efficiency of the proposed model is measured based on derivative parameters, such as F‐1‐score, recall, precision and accuracy, with corresponding confusion matrix and ROC. Further, the classification is compared with other traditional approaches to predict the accuracy of the proposed model with traditional methods.
Abstract The medical information system is rich in datasets, but no intelligent systems can easily analyse the disease. Recently, ML (Machine Learning)‐based algorithms have acted as a handy diagnostic tool to identify whether a person is affected by thyroid or not. However, they produced classification with low accuracy and led to misclassification. Hence, the proposed system combines quantum computing with ML techniques to enhance computational power and precision. The system employs modified QPSO (Quantum Particle Swarm Optimisation) for feature selection since its searching performance is better than that of conventional PSO for selecting the optimum global position of the particle, thus selecting the relevant feature. Whereas, the QSVM (Quantum Support Vector Machine) is implemented for more accurate classification than classical SVM, as it tends to capture complex patterns in data produced due to high dimensional feature space applied by quantum kernel functions. This combination of modified QPSO and QSVM tends to increase the performance accuracy significantly. The efficiency of the proposed model is measured based on derivative parameters, such as F‐1‐score, recall, precision and accuracy, with corresponding confusion matrix and ROC. Further, the classification is compared with other traditional approaches to predict the accuracy of the proposed model with traditional methods.
The healthcare systems produce a tremendous volume of medical data which may be in the form of patient records, laboratory test results and diagnostic reports. It is quite necessary to analyze such data accurately and efficiently in order to detect diseases early and plan appropriate treatment . Conventional machine learning methods have a tendency to struggling with large-dimensional and complicated medical data. To address these shortcomings, this project suggests a hybrid Quantum Machine Learning (QML) scheme of analyzing medical data and diagnosing a disease. The suggested system combines the classical data preprocessing with the quantum-enhanced data learning algorithms including Variational Quantum Circuits (VQC) and Quantum Support Vector Machines (QSVM). Medical data is first processed by cleaning, normalizing and encoding to quantum states. Complex patterns and correlations that traditional algorithms can hardly be able to learn are then learned with the quantum model. The measures used to judge the performance are accuracy, precision, recall and F1-score. The results of the experimental process show that the model based on QML yields better diagnostic accuracy than the conventional machine learning. This is where there is great potential in real time medical decision support systems, with the future prediction of disease and the personalized solution of care. The suggested framework indicates the potential and benefits of switching to quantum computing methods in healthcare analytics.
Abstract Accurate early identification and treatment of cardiovascular diseases can prevent heart failure problems and reduce mortality rates. This study aims to use quantum learning to predict heart problems to increase the accuracy of traditional prediction and classification methods. Machine learning (ML) and deep learning (DL) techniques need quantum learning to quickly and accurately analyze massive volumes of complex data. With quantum computing, the suggested DL and ML algorithms can change their predictions on the basis of changes in the dataset. This approach could help with the early and accurate detection of chronic diseases. The Cleveland heart disease dataset is undergoing preliminary processing to validate missing values to increase the precision rate and prevent incorrect forecasts. This study examined the feasibility of employing and deploying a quantum ML (QML) framework via cloud computing to categorize cardiac conditions. The research was divided into four sections. First, the principal component analysis was used to preprocess the Cleveland dataset, recursive feature elimination was used to select features, and min–max normalization was used to give the dataset a high-dimensional value. Second, we compared traditional classifiers, such as support vector machine (SVM) and artificial neural network, with the quantum approach to verify the quantum approach’s efficiency. Third, we examined two unique QML classification methods: quantum neural networks (QNNs) and quantum SVM (QSVM). Fourth, bagging-QSVM was developed and deployed as an ensemble learning model. Experimental results using the QNN show an accuracy of 77%, a precision of 76%, a recall of 73%, and an F 1 score of 75%. With an accuracy of 85%, a precision of 79%, a recall of 90%, and an F 1-score of 84%, the QSVM method demonstrated a much better performance than the QNN. Particularly, the Bagging_QSVM model exhibited an outstanding performance, with a flawless score of 100% across all critical performance measures. The study shows that the bagging method for ensemble learning is a solid way of increasing the accuracy of quantum method predictions.
Early and accurate disease prediction is a major challenge in modern healthcare systems. Delayed diagnosis often leads to higher treatment costs and lower patient survival rates. Artificial Intelligence (AI) and Machine Learning (ML) techniques are widely used to help with medical decision-making by analyzing complex healthcare datasets. However, traditional machine learning models often face issues with inefficient feature selection, poor hyperparameter tuning, and slow convergence during optimization. This is especially true when working with high-dimensional medical data. To tackle these challenges, this paper presents an AI-based disease prediction framework that uses quantum-inspired optimization techniques. This approach combines classical machine learning classifiers with optimization strategies based on quantum computing principles, such as probabilistic state representation and superposition-based search. These quantum-inspired methods allow for efficient exploration of the solution space, which leads to better feature selection and optimized model parameters. We evaluate the proposed framework using a publicly available healthcare dataset from Kaggle. We compare the performance of traditional machine learning models and quantum-inspired optimized models using accuracy, precision, recall, and F1-score metrics. The experimental results show that the quantum-inspired optimized model consistently performs better than conventional approaches. This study demonstrates that quantum-inspired optimization provides a practical and scalable solution for improving AI-driven disease prediction systems without the need for actual quantum computing hardware.
Ovarian cancer remains one of the leading causes of cancer-related mortality among women worldwide, largely because most cases are diagnosed at advanced stages. Current diagnostic tools and classical machine learning models often show limited sensitivity and specificity, particularly when applied to the complex, high-dimensional datasets required for accurate prediction. These limitations highlight the need for more powerful computational approaches capable of extracting subtle diagnostic patterns. The aim of this study is to enhance the accuracy and efficiency of ovarian cancer diagnosis by using quantum-inspired machine learning approach such as Quantum Support Vector Machines (QSVMs). The study utilized QSVMs to analyse clinical and genomic datasets, leveraging quantum mechanics principles like superposition and entanglement to process data more effectively than traditional machine learning models. The QSVM model was developed, trained, and evaluated using performance metrics such as accuracy, sensitivity, specificity, and processing efficiency. The results demonstrated that QSVMs achieved a diagnostic accuracy of 92%, outperforming traditional support vector machines and other classical models. Additionally, the processing time for diagnosis was reduced from 45 minutes to 20 minutes, providing a faster and more reliable workflow. The QSVMs also excelled in analysing multi-omics data, enabling the identification of early-stage ovarian cancer biomarkers and supporting personalized treatment strategies. In conclusion, this study demonstrates the potential of QSVMs to transform ovarian cancer diagnostics by addressing key limitations of conventional methods. The findings underscore the importance of adopting quantum-inspired machine learning in medical applications and encourage further exploration of these advanced algorithms in improving healthcare outcomes.
Quantum computing is a promising new approach to tackle the complex real-world computational problems by harnessing the power of quantum mechanics principles. The inherent parallelism and exponential computational power of quantum systems hold the potential to outpace classical counterparts in solving complex optimization problems, which are pervasive in machine learning. Quantum Support Vector Machine (QSVM) is a quantum machine learning algorithm inspired by classical Support Vector Machine (SVM) that exploits quantum parallelism to efficiently classify data points in high-dimensional feature spaces. We provide a comprehensive overview of the underlying principles of QSVM, elucidating how different quantum feature maps and quantum kernels enable the manipulation of quantum states to perform classification tasks. Through a comparative analysis, we reveal the quantum advantage achieved by these algorithms in terms of speedup and solution quality. As a case study, we explored the potential of quantum paradigms in the context of a real-world problem: classifying pancreatic cancer biomarker data. The Support Vector Classifier (SVC) algorithm was employed for the classical approach while the QSVM algorithm was executed on a quantum simulator provided by the Qiskit quantum computing framework. The classical approach as well as the quantum-based techniques reported similar accuracy. This uniformity suggests that these methods effectively captured similar underlying patterns in the dataset. Remarkably, quantum implementations exhibited substantially reduced execution times demonstrating the potential of quantum approaches in enhancing classification efficiency. This affirms the growing significance of quantum computing as a transformative tool for augmenting machine learning paradigms and also underscores the potency of quantum execution for computational acceleration.
Abstract Machine learning has emerged as a promising method for predicting breast cancer using quantum computation techniques. Quantum machine learning algorithms, such as quantum support vector machines (QSVMs), are demonstrating superior efficiency and economy in tackling complex problems compared to traditional machine learning methods. When compared with classical support vector machine, the quantum machine produces remarkably accurate results. The suggested quantum SVM model in this study effectively resolved the binary classification problem for diagnosing malignant breast cancer. This work introduces an enhanced approach to breast cancer diagnosis by integrating QSVM with elitist non‐dominated sorting genetic optimization (ENSGA), leveraging the strengths of both techniques to achieve more accurate and efficient classification results. ENSGA plays a crucial role in optimising QSVM parameters, ensuring that the model attains the best possible classification accuracy while considering multiple objectives simultaneously. Moreover, the quantum kernel estimation method demonstrated exceptional performance by achieving high accuracy within an impressive execution time of 0.14 in the IBM QSVM simulator. The seamless integration of quantum computation techniques with optimisation strategies such as ENSGA highlights the potential of quantum machine learning in revolutionising the field of healthcare, particularly in the domain of breast cancer diagnosis.