Sepsis and the development of multi-organ failure still constitute significant causes of death in the ICU, hence the necessity to develop predictive systems that could detect the fact of subtle physiological deviations before clinical deterioration. Despite the potential of classical machine learning models, including logistic regression, random forests and deep neural networks to predict sepsis, they frequently encounter challenges with large-dimensional ICU time-series data, nonlinear interaction of features and ability to predict sepsis at an early stage. These also models are sensitive to missing values and have limited cross-hospital generalizability and high rates of false-alarms which makes them difficult to implement in clinical settings, this paper proposes a Quantum-Assisted Early Warning System (QAEWS) based on quantum feature encoding and hybrid quantum-classical based learning models that provides better early sepsis and organ dysfunction detection. Amplitude and phase encoding the ICU physiological measures, lab measurements, and short-term vital sign traces are encoded into quantum-enhanced representations, which allow complexer modeling of complicated time-dependent relationships in a high-dimensional Hilbert space. The predictive architecture combines the Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN) and supplemented by the classical neural layers to improve classification accuracy and false alerts. Model performance was compared with the Logistic Regression, Random Forest, XGBoost, and LSTM models using the same feature sets in the MIMIC-IV and eICU datasets. The quantum-assisted framework achieved a 7–12% increase in AUROC, a 14–18% reduction in false alarms, and provided predictions 1.5–2.8 hours earlier than classical baselines. Sensitivity at 85% specificity improved by 10–15%. These results demonstrate that quantum-enhanced feature spaces offer superior discrimination of early sepsis patterns, establishing QAEWS as a robust and clinically valuable approach for real-time ICU early warning.
Sepsis and the development of multi-organ failure still constitute significant causes of death in the ICU, hence the necessity to develop predictive systems that could detect the fact of subtle physiological deviations before clinical deterioration. Despite the potential of classical machine learning models, including logistic regression, random forests and deep neural networks to predict sepsis, they frequently encounter challenges with large-dimensional ICU time-series data, nonlinear interaction of features and ability to predict sepsis at an early stage. These also models are sensitive to missing values and have limited cross-hospital generalizability and high rates of false-alarms which makes them difficult to implement in clinical settings, this paper proposes a Quantum-Assisted Early Warning System (QAEWS) based on quantum feature encoding and hybrid quantum-classical based learning models that provides better early sepsis and organ dysfunction detection. Amplitude and phase encoding the ICU physiological measures, lab measurements, and short-term vital sign traces are encoded into quantum-enhanced representations, which allow complexer modeling of complicated time-dependent relationships in a high-dimensional Hilbert space. The predictive architecture combines the Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN) and supplemented by the classical neural layers to improve classification accuracy and false alerts. Model performance was compared with the Logistic Regression, Random Forest, XGBoost, and LSTM models using the same feature sets in the MIMIC-IV and eICU datasets. The quantum-assisted framework achieved a 7–12% increase in AUROC, a 14–18% reduction in false alarms, and provided predictions 1.5–2.8 hours earlier than classical baselines. Sensitivity at 85% specificity improved by 10–15%. These results demonstrate that quantum-enhanced feature spaces offer superior discrimination of early sepsis patterns, establishing QAEWS as a robust and clinically valuable approach for real-time ICU early warning.
Early and accurate anomaly detection in Intensive Care Unit (ICU) monitoring is vital for improving patient outcomes and reducing mortality. Traditional machine learning techniques often face challenges in handling the noisy, high-dimensional, and limited datasets characteristic of ICU physiological signals. In this study, we present an early investigation of a quantum-enhanced framework employing a classically simulated Quantum Support Vector Machine (QSVM) to improve the detection of critical anomalies in real-world ICU time-series data. Utilizing the publicly available PhysioNet 2012 Challenge dataset, we extract comprehensive statistical and temporal features from vital signs including heart rate, blood pressure, respiratory rate, and oxygen saturation. Our QSVM model leverages a quantum kernel mapped via Qiskit’s ZZFeatureMap and is rigorously benchmarked against classical machine learning classifiers such as Support Vector Machines with radial basis function kernels, Random Forest, and XGBoost. Experimental results demonstrate that the QSVM shows competitive and robust classification performance, particularly excelling in scenarios with limited and noisy data, with an AUC of 0.92, particularly excelling in scenarios with limited and noisy data. Furthermore, the quantum kernel’s ability to implicitly project data into a high-dimensional Hilbert space provides enhanced separability of complex physiological patterns. This work constitutes one of the first simulation-based applications of quantum machine learning to critical care anomaly detection, underscoring the promise of hybrid quantum-classical approaches for advancing real-time clinical decision support systems.
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.
Early detection of clinical deterioration is crucial for better patient outcomes, and current diagnostic models struggle to model the complex interactions observed in nonlinear patterns in multimodal medical data while meeting the performance requirements for real-time operation. This paper proposes a Real-Time Hybrid Quantum-Classical Diagnostic Framework (HQCDF) to improve early disease detection by combining a Quantum Variational Biomarker Embedding module (QVBE), a Classical Temporal-Clinical Learning module (CTCLM), and a novel Quantum-Classical Diagnostic Fusion mechanism (QCDF). A semi-synthetic multimodal dataset, Med-EarlyQ, physiological time series, lab biomarkers, and clinical scores was built to evaluate. The hybrid model has been trained using End-to-end differentiable quantum-classical optimization via the parameter-shift rule. Results showed that HQCDF obtained 96% accuracy, 0.93 F1-score, and 0.95 AUROC to outperform state-of-the-art classical deep learning models and existing hybrid approaches by 6~15% while controlling the real-time inference latency to 18 ms. Analysis revealed that the QCDF module contributed to increased robustness against quantum noise and missing clinical information, and to QE's increased sensitivity to subtle trends in disease in its early stages. The results show that the proposed hybrid framework is a promising evolution, addressing the call for next-generation, real-time, and resource-efficient diagnostic systems with great potential for early intervention and clinical decision-support impact.
Parkinson’s disease (PD) is a progressive neurodegenerative disorder affecting millions worldwide, where early and accurate diagnosis plays a critical role in improving patient outcomes. While classical machine learning methods have shown strong performance in PD classification using clinical and acoustic data, they remain limited in capturing complex nonlinear feature interactions in high-dimensional spaces. This study explores the application of quantum machine learning (QML) techniques for PD prediction, focusing on Variational Quantum Classifiers (VQC) and Quantum Support Vector Machines (QSVM), and compares their performance with strong classical baselines including Logistic Regression, SVM, Random Forest, Grid Search–optimized models, and XGBoost. Experiments were conducted using stratified train–test splits and validated through cross-validation and statistical testing. Results show that classical models achieve competitive performance, with XGBoost reaching 91.9% accuracy. However, quantum models demonstrate slightly improved performance, with the VQC using EfficientSU2 achieving the highest accuracy of 93.1%, followed by the Real Amplitudes VQC (92.6%) and QSVM (92.2%). These improvements are consistent across evaluation metrics including F1-score and recall. Statistical analysis using 10-fold cross-validation indicates that the VQC (EfficientSU2) shows a significant improvement over classical baselines ( p = 0.031), while other quantum models do not achieve statistical significance over XGBoost ( p > 0.05). McNemar’s test on the independent test set confirms no significant difference in final prediction distributions between quantum and classical models. Overall, the findings suggest that quantum machine learning models are competitive with state-of-the-art classical approaches, offering marginal but consistent performance gains. This study highlights the potential of quantum-enhanced learning frameworks for biomedical classification tasks, while also emphasizing that their advantages remain incremental rather than definitive under current simulation-based implementations.
Chronic diseases—such as diabetes, cardiovascular disorders, and chronic respiratory conditions—account for over 70% of global deaths annually, with late diagnosis being a primary contributor to poor outcomes. While machine learning (ML) models have shown promise in early detection, they often suffer from limited generalizability, data heterogeneity, and insufficient interpretability in clinical settings. This paper introduces a novel hybrid quantum-classical machine learning (HQML) framework that synergistically combines the pattern recognition power of classical deep neural networks with the high-dimensional optimization capabilities of quantum algorithms to enhance diagnostic accuracy, robustness, and early signal detection in chronic disease prediction. Using multimodal electronic health record (EHR) data—including clinical, genomic, and lifestyle variables—we train a quantum-enhanced feature selector followed by a classical interpretable classifier (e.g., SHAP-augmented XGBoost). The quantum component leverages a variational quantum circuit to identify non-linear, high-order feature interactions that classical methods often miss. Validated on datasets from the National Health and Nutrition Examination Survey (NHANES) and UK Biobank, our model achieves 94.7% AUC for type 2 diabetes prediction five years before clinical diagnosis—outperforming state-of-the-art baselines by 6.2%. Crucially, the framework maintains interpretability through post-hoc explainability and reduces data bias via fairness-aware quantum embedding. This research bridges quantum computing and public health, offering a scalable, ethically grounded diagnostic paradigm. By enabling earlier, more accurate, and equitable predictions, the HQML framework has significant potential to transform preventive care and reduce the global burden of chronic disease.
Early diagnosis of chronic diseases is paramount to improving patient survival rates, optimizing treatment trajectories, and mitigating escalating healthcare expenditures. While the recent explosion of heterogeneous healthcare data presents robust opportunities for predictive modeling, it simultaneously introduces severe computational bottlenecks regarding data dimensionality and non-linear complexity. This paper proposes a novel, high-performance hybrid prediction framework that seamlessly fuses classical machine learning topologies with Variational Quantum Circuits (VQCs) within a Quantum Machine Learning (QML) paradigm. The proposed architecture targets the multi-classification and risk profiling of five high-prevalence chronic conditions: diabetes, cardiovascular disease, breast cancer, chronic liver disease, and chronic kidney disease This project introduces “Quantum Health,” a Quantum Machine Learning (QML)-based multi disease prediction system that integrates classical and quantum computing to enhance diagnostic performance. It employs five quantum-enhanced models: Quantum Random Forest for breast cancer, Quantum Neural Network for diabetes, Quantum k-Nearest Neighbors (QkNN) for heart disease, Quantum AdaBoost for liver disease, and Quantum Naive Bayes for kidney disease. The models are evaluated using both classical methods and PennyLane simulations, enabling direct comparison of performance. The system is deployed as a Flask-based web application featuring a user-friendly dashboard, disease-specific prediction modules, prediction history with CSV export, and an analytics dashboard comparing classical and quantum accuracies. Each module provides predictions, confidence scores, and feature importance insights. Unlike monolithic classical models, our approach incorporates a parameterized quantum layer that maps low-dimensional classical medical vectors into high-dimensional Hilbert spaces, capturing intricate, non-linear correlations that evade classical kernels. Experimental evaluations executed on simulated quantum backends demonstrate that the proposed Quantum Hybrid Model drastically outperforms conventional baselines, achieving an overall classification accuracy of 96.8% and an Area Under the Curve (AUC) of 98.4%. The findings establish that integrating quantum circuit state vectors with ensemble classical architectures provides a scalable, computationally efficient, and highly generalizable paradigm for next-generation clinical decision support systems.