Malaria is one of the global health threats that are of utmost concern, and it is paramount that the disease is identified early in time so that it can be treated and prevented to cause serious consequences. The study will provide a pioneering comparative study of quantum computing and classical machine learning to predict malaria, which will deal with healthcare diagnostics by using superior computational intelligence. Using a set of 1,622 patient records, including 15 clinical features, we compared the performance of three classical (Random Forest, Gradient Boosting and Support Vector Machine) and three quantum-inspired (Quantum Support Vector Classifier, Variational Quantum Classifier and Quantum Neural Network) classifiers using a large-scale clinical data set. The classical models performed exceptionally well with the Random Forest and Gradient Boosting models exhibiting 100 percent accuracy and quantum models performing well with QSVC with 97.85 percent accuracy based on dimensional reduction of features. Principal Component Analysis downsized the feature space by a factor of 15 to 8 dimensions and kept 63.51% variance, so that quantum circuit implementation is efficient. We find that classical ensemble methods are now more competitive than the quantum ones in malaria prediction, but quantum models are also competitive and use much less computing resources. The article identifies the practical applications in the resourcelimited healthcare facilities and lays the groundwork to hybrid quantum-classical architectures within the clinical decision support framework, which can advance to better patient outcomes and assist everyone to access high-quality healthcare services.
Malaria is one of the global health threats that are of utmost concern, and it is paramount that the disease is identified early in time so that it can be treated and prevented to cause serious consequences. The study will provide a pioneering comparative study of quantum computing and classical machine learning to predict malaria, which will deal with healthcare diagnostics by using superior computational intelligence. Using a set of 1,622 patient records, including 15 clinical features, we compared the performance of three classical (Random Forest, Gradient Boosting and Support Vector Machine) and three quantum-inspired (Quantum Support Vector Classifier, Variational Quantum Classifier and Quantum Neural Network) classifiers using a large-scale clinical data set. The classical models performed exceptionally well with the Random Forest and Gradient Boosting models exhibiting 100 percent accuracy and quantum models performing well with QSVC with 97.85 percent accuracy based on dimensional reduction of features. Principal Component Analysis downsized the feature space by a factor of 15 to 8 dimensions and kept 63.51% variance, so that quantum circuit implementation is efficient. We find that classical ensemble methods are now more competitive than the quantum ones in malaria prediction, but quantum models are also competitive and use much less computing resources. The article identifies the practical applications in the resourcelimited healthcare facilities and lays the groundwork to hybrid quantum-classical architectures within the clinical decision support framework, which can advance to better patient outcomes and assist everyone to access high-quality healthcare services.
Millions of individuals are affected by asthma globally in that there is an urgent need to have accurate prediction systems in order to allow an early intervention and increase the patient outcome. This paper provides an extensive comparison of classical machine learning models and quantum machine learning methods to predict asthma and assess their viability to be used in clinical practice. Our data consisted of 50,000 patient records, consisting of 16 clinical features (symptoms, demographics, and severity indicators) and applied six classical models (Random Forest, Gradient Boosting, Support Vector Machine, Neural Network, Logistic Regression, K-Nearest Neighbors). We used standardised preprocessing and evaluation procedures to test Quantum Support Vector Machine on50,000patient records. Quantum SVM was found to be most accurate with 75.69 and closely second with classical SVM, Gradient Boosting and Logistic Regression, which had 75.16. All the most effective models had the perfect recall(100%), which provided complete coverage of the asthma cases. Random Forest and Neural Network had an accuracy of 74.91 and 75.06 respectively, whereas K-Nearest Neighbors had an accuracy of 70.49 %. Such predictive characteristics were the inability to inhale properly, dry cough, and age factor. Classical models had better computational time of 0.01-19.5 seconds to train and Quantum SVM took 45 seconds because of overhead in quantum circuit simulations. These results indicate that both classical ensemble and quantum models are clinically feasible to predict asthma, and classical models are ready to deploy the predictions in practice and quantum models have a potential in the future as quantum hardware advances. The study will enhance AI-facilitated healthcare innovation and will help in the better management of respiratory diseases.
Cardiovascular diseases remain the leading cause of mortality globally, necessitating accurate diagnostic tools for early detection and intervention. This paper presents a comprehensive comparative and hybrid analysis of quantum and classical machine learning models for heart disease prediction using the UCI Cleveland dataset. We implement classical baseline models including Logistic Regression, Random Forest, Support Vector Machine (SVM), and XGBoost, alongside quantum models: Quantum Support Vector Machine (QSVM) with statevector fidelity kernels, Variational Quantum Classifier (VQC) with data-reuploading ansätze, and Quantum Convolutional Neural Networks (QCNN) with parametric pooling. The primary contribution is the development of a hybrid quantum classical ensemble meta-classifier that fuses predictions from all classical and quantum learners using XGBoost as a meta learner. Experiments conducted in Google Colab using Qiskit 1.2+ and Penny Lane 0.36+ demonstrate that the proposed hybrid ensemble achieves superior performance with 98.33accuracy, 100perfect AUC-ROC of 1.0, substantially outperforming standalone classical models (best: 88.33(best: 80hybrid model’s superiority. These results underscore the potential of quantum feature spaces combined with classical decision boundaries in healthcare prediction systems, and demonstrate the immediate utility of hybrid quantum-classical integration in medical diagnostics on near-term quantum hardware.
Prompt diagnosis of disease is a strong driver for decreasing mortality, improving patient care and healthcare expenditures. Classical machine learning (ML), which has shown great potential in bolstering predictive modeling throughout oncology, cardiology, and neurology, runs the risk of excessive data dimensionality along with high computational complexity. Quantum computing is an exciting new paradigm for computation that takes advantage of superposition and entanglement which can be beneficial in learning patterns in high-dimensional spaces and for kernel-based learning. This review provides a systematic summary of the preliminary work on quantum machine learning (QML) with empirical applications to early disease diagnosis, while emphasizing its theoretical foundations, algorithmic developments and application in healthcare. We scope the conversation on quantum support vector machines, variational quantum classifiers, quantum kernels and hybrid architectures for a quantum classical. We also examine current hardware limitations in the NISQ era and assess how close these methods are to being ready for translation into public health applications. Although widespread clinical application is still far away, hybrid QML approaches are finding utility in low-data and high-complexity settings. We conclude with a discussion of research challenges, regulation and strategy towards realizing such quantum-enhanced precision diagnostics through U.S. public health innovation initiatives
Blood glucose is one of the key indicators influencing the body’s metabolic performance. Accurate prediction and proactive management of this parameter are of paramount clinical importance. In this context, the analysis and evaluation of quantum machine learning (QML) algorithms for processing medical data and enhancing the predictive accuracy of health-related models have emerged as cutting-edge research directions in recent studies. Leveraging the principles of quantum physics and computation, QML algorithms serve as a promising alternative to classical methods for predicting chronic diseases. By mapping classical data into a quantum feature space and exploiting intrinsic quantum properties such as superposition and entanglement, these algorithms significantly enhance the ability to detect complex and latent patterns within datasets. In this study, the dataset was first preprocessed using Principal Component Analysis (PCA) for dimensionality reduction and noise suppression. Subsequently, the predictive performance of two quantum machine learning models—the Quantum Support Vector Machine (QSVM) and the Variational Quantum Circuit (VQC)—was evaluated and compared with that of the classical Support Vector Machine (SVM) for blood glucose level prediction. The results indicate that the QSVM model achieves a prediction accuracy of 90%, outperforming the classical SVM, which attained an accuracy of 74.4%. Moreover, the hybrid classical–quantum VQC model demonstrated the highest predictive performance, achieving an accuracy of 94.2%, thereby establishing it as the most effective approach for blood glucose level forecasting in this experimental setting.
Quantum Machine Learning (QML) is fast becoming a powerful instrument in medical diagnostics because it can process high-dimensional data and small samples efficiently. This paper is a detailed analysis of QML models used for disease prediction, with a relative comparison of Variational Quantum Classifiers (VQC), Quantum Convolutional Neural Networks (QCNN), and Quantum Support Vector Machines (QSVM) in clinical environments. We suggest a new multimodal quantumclassical hybrid approach that analyzes electronic health records (EHR), medical imaging, and biosignal data by means of dedicated QML pipelines with late fusion for ultimate decisionmaking. Recent IEEE literature is integrated in experimental performance glimpses to demonstrate the merits, difficulties, and viability of QML in real-world healthcare applications. The envisioned architecture provides the foundation for future applications of quantum-enhanced diagnostics with existing quantum simulators as well as emerging hardware.
Diabetes remains one of the common chronic diseases around the world and finding a good early prediction from clinical and lifestyle data is very important for timely help. Classical machine learning models such as regression, decision trees and random forests have been used for this purpose but the complicated non‑linear connections among clinical features might need richer ways of representation. Quantum Machine Learning (QML) gives an approach using quantum feature encoding and variational circuits to find patterns that are expensive to express with classical methods. In this study we show a diabetes prediction pipeline that compares baseline models—logistic regression, decision tree and random forest—with two quantum classifiers built in Qiskit: a Quantum Support Vector Classifier (QSVC) and a Variational Quantum Classifier (VQC). Using the Pima Indians Diabetes dataset we test all models on accuracy, precision, recall and F1‑score. The results show that the classical Random Forest classifier reaches the accuracy (0.73) beating both quantum classifiers. The Quantum Support Vector Classifier (QSVC 0.66) and the Variational Quantum Classifier (VQC 0.65) are still competitive. Do not outdo the classical baselines. These results agree with reports that in the present Noisy Intermediate‑Scale Quantum (NISQ) era quantum models usually match rather than surpass well‑tuned classical approaches on standard health‑care data and that a real quantum advantage will need larger data, deeper circuits and testing, on real quantum hardware.