Abstract Chronic Kidney Disease (CKD) is a progressive and potentially fatal condition that is often diagnosed at advanced stages due to its asymptomatic nature in early progression. Accurate early prediction is challenging because of limited, heterogeneous clinical data and complex nonlinear relationships among biomarkers. This paper proposes HCF-QNet, a hybrid quantum–classical learning framework for robust CKD prediction. The proposed approach integrates three complementary components: optimized feature selection, contrastive representation learning, and variational quantum classification. Initially, a Grey Wolf Optimizer (GWO) is employed to select the most informative clinical features, reducing redundancy and dimensionality. The selected features are then transformed into a compact latent space using a supervised contrastive encoder, which enhances intra class compactness and inter class separability. These embeddings are subsequently processed by an 8-qubit Variational Quantum Neural Network (VQNN) with ring-based entanglement, enabling expressive nonlinear modeling beyond classical classifiers. A two-stage training strategy is adopted, where the encoder is contrastively pretrained and frozen during quantum fine-tuning to ensure stable optimization. The model is evaluated on the UCI CKD dataset using five fold cross-validation. Experimental results demonstrate an average accuracy of 99.75%, achieving better predictive performance than most existing classical machine learning and deep learning approaches. The results highlight the potential of hybrid quantum–classical models for accurate medical diagnosis on small and heterogeneous clinical datasets.
Abstract Chronic Kidney Disease (CKD) is a progressive and potentially fatal condition that is often diagnosed at advanced stages due to its asymptomatic nature in early progression. Accurate early prediction is challenging because of limited, heterogeneous clinical data and complex nonlinear relationships among biomarkers. This paper proposes HCF-QNet, a hybrid quantum–classical learning framework for robust CKD prediction. The proposed approach integrates three complementary components: optimized feature selection, contrastive representation learning, and variational quantum classification. Initially, a Grey Wolf Optimizer (GWO) is employed to select the most informative clinical features, reducing redundancy and dimensionality. The selected features are then transformed into a compact latent space using a supervised contrastive encoder, which enhances intra class compactness and inter class separability. These embeddings are subsequently processed by an 8-qubit Variational Quantum Neural Network (VQNN) with ring-based entanglement, enabling expressive nonlinear modeling beyond classical classifiers. A two-stage training strategy is adopted, where the encoder is contrastively pretrained and frozen during quantum fine-tuning to ensure stable optimization. The model is evaluated on the UCI CKD dataset using five fold cross-validation. Experimental results demonstrate an average accuracy of 99.75%, achieving better predictive performance than most existing classical machine learning and deep learning approaches. The results highlight the potential of hybrid quantum–classical models for accurate medical diagnosis on small and heterogeneous clinical datasets.
Hybrid Quantum Neural Networks (HQNNs) have recently emerged as a promising paradigm for near-term quantum machine learning. However, their practical performance strongly depends on design choices such as classical-to-quantum data encoding, quantum circuit architecture, measurement strategy and shots. In this paper, we present a comprehensive design space exploration of HQNNs for Chronic Kidney Disease (CKD) diagnosis. Using a carefully curated and preprocessed clinical dataset, we benchmark 625 different HQNN models obtained by combining five encoding schemes, five entanglement architectures, five measurement strategies, and five different shot settings. To ensure fair and robust evaluation, all models are trained using 10-fold stratified cross-validation and assessed on a test set using a comprehensive set of metrics, including accuracy, area under the curve (AUC), F1-score, and a composite performance score. Our results reveal strong and non-trivial interactions between encoding choices and circuit architectures, showing that high performance does not necessarily require large parameter counts or complex circuits. In particular, we find that compact architectures combined with appropriate encodings (e.g., IQP with Ring entanglement) can achieve the best trade-off between accuracy, robustness, and efficiency. Beyond absolute performance analysis, we also provide actionable insights into how different design dimensions influence learning behavior in HQNNs.
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.
The two leading causes of chronic kidney disease (CKD) are excessive blood pressure and diabetes. Researchers worldwide utilize the rate of globular filtration and kidney inflammation biomarkers to identify chronic kidney disease that gradually reduces renal function. The mortality rate for CKD is high, and thus, a person with this illness is more likely to pass away at a younger age. Healthcare professionals must diagnose the various illnesses connected to this deadly disease as promptly as possible to lighten the impact of CKD. A quantum machine learning (QML) based technique is presented in this research to help with the early diagnosis and prognosis of CKD. The proposed research comprises four phases: data pre-processing, data augmentation, feature selection, and classification. In the first phase, Kalman filter and data normalization techniques are applied to handle the missing and noisy data. In the second phase, data augmentation uses sparse autoencoders to balance the data for smaller classes. In the third phase, LASSO shrinkage is used to select the significant features in the dataset. Variational Quantum classifiers, a supervised QML technique, are employed in the classification phase to classify chronic kidney diseases. The proposed system has been evaluated on the UCI dataset, which comprises 400 CKD patients in the early stages with 25 attributes. The suggested system was assessed using F1-score, precision, recall, and accuracy as evaluation metrics. With a 99.2% classification accuracy, it was found that this model performed better than the other traditional classifiers used for chronic kidney disease classification.
Chronic Kidney Disease (CKD) does not present symptoms in early phases so often goes undiagnosed. This increases risk of severe clinical outcomes. Hence, efficient patient management demands prompt diagnosis and an accurate diagnosis. Classical machine learning models effectively diagnose CKD but they struggle with learning representations and interpretability, which undermines the potential for clinical deployment. Quantum Machine Learning (QML) enables greater feature expressivity with quantum features spaces. However, quantum classifiers executed on Noisy Intermediate-scale Quantum (NISQ) devices may be susceptible to noise-induced instability. This work proposes an Explainable Bagging-Based Quantum Support Vector Classification (EB-QSVC) framework which enhances CKD detection robustness and interpretability. This framework uses classical preprocessing followed by dimensionality reduction with Principal Component Analysis, qubit feature encoding for quantum mechanics and ensemble bagging to suppress NISQ. The use of explainable artificial intelligence methods is employed to deliver clinically useful information.The experiment shows that the accuracy of a QSVC with no extended-attribute is about 0.70, while the proposed EB-QSVC achieves accuracy up to 0.95 matching with the strong classical baselines. The results show that ensemble-based and interpretable quantum learning methods can stabilize quantum models subjected to the hardware limitations at hand, thus providing a reliable decision-support framework for healthcare.
In this study, we develop and compare quantum and classical machine learning-based chronic kidney disease prediction models. We used the "Chronic_Kidney_Disease Data Set" of the UCI Machine Learning Repository. We performed data preprocessing and applied feature engineering techniques to select the best features. We developed two quantum machine learning-based models and two classical machine learning-based models. We used a hybrid classical-quantum environment for building quantum machine learning models. Finally, we compared the performances of all four models. We found that the Quantum Support Vector Machine performs best among the quantum models. The model’s accuracy was 95% with a k-fold cross-validation score of 94.5% and an ROC-AUC score of 0.987. Among the classical models, the Support Vector Machine showed the highest performance with an accuracy of 92.5%, a k-fold cross-validation score of 93.9%, and a ROC-AUC score of 0.974. Overall, the Quantum Support Vector Machine outperformed all other developed models in terms of accuracy and validation scores. If the quantum models can be executed in a quantum computer instead of a hybrid environment, the models will exhibit higher accuracy and faster execution time. As the models are precisely predicting chronic kidney disease with high accuracy, we believe this study will act as an inspiring framework in the less-investigated field of quantum machine learning-based chronic kidney disease prediction.