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.
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.
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.
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.
Background The kidney is one of the most essential organs in our body, and any problems with it are perilous, hence, the early diagnosis of chronic kidney disease (CKD) is crucial. support vector machine (SVM), a popular machine learning (ML) technique, is an effective solution for building an early CKD diagnosis system. Nowadays ML techniques like SVM are often combined with upcoming quantum computing technology to improve over classical ML. Methods This research uses classical SVM (CSVM) and Quantum SVM (QSVM) to develop a CKD diagnosis system and compare the efficiency of the two diagnosis systems. This research performs different preprocessing on a CKD dataset. Based on the analysis and preprocessing, two data optimization approaches principal component analysis (PCA) and singular value decomposition (SVD) are applied to generate two optimized datasets. Moreover, classification is done on these two datasets by utilizing both CSVM and QSVM. Findings The comprehensive analysis of various techniques reveals that PCA outperforms SVD when paired with both CSVM and QSVM. Utilizing PCA, CSVM achieves a remarkable accuracy of 98.75 %, while QSVM achieves 87.5 % accuracy. In contrast, by utilizing SVD, both CSVM and QSVM achieve relatively lower accuracies, with CSVM achieving 96.25 % accuracy and QSVM achieving 60 % accuracy. Interpretation The final assessment of this research confirms that QSVM requires more time in classical experimental settings compared to CSVM. Furthermore, the research aims to make it easier to catch CKD early by providing reliable and efficient diagnosis methods. At the same time, it opens the door for trying out new quantum ML ideas in healthcare down the line.
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.
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.