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.
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.
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.
Nowadays, quantum machine learning is attracting great interest in a wide range of fields due to its potential superior performance and capabilities. The massive increase in computational capacity and speed of quantum computers can lead to a quantum leap in the healthcare field. Heart disease seriously threatens human health since it is the leading cause of death worldwide. Quantum machine learning methods can propose effective solutions to predict heart disease and aid in early diagnosis. In this study, an ensemble machine learning model based on quantum machine learning classifiers is proposed to predict the risk of heart disease. The proposed model is a bagging ensemble learning model where a quantum support vector classifier was used as a base classifier. Furthermore, in order to make the model’s outcomes more explainable, the importance of every single feature in the prediction is computed and visualized using SHapley Additive exPlanations (SHAP) framework. In the experimental study, other stand-alone quantum classifiers, namely, Quantum Support Vector Classifier (QSVC), Quantum Neural Network (QNN), and Variational Quantum Classifier (VQC) are applied and compared with classical machine learning classifiers such as Support Vector Machine (SVM), and Artificial Neural Network (ANN). The experimental results on the Cleveland dataset reveal the superiority of QSVC compared to the others, which explains its use in the proposed bagging model. The Bagging-QSVC model outperforms all aforementioned classifiers with an accuracy of 90.16% while showing great competitiveness compared to some state-of-the-art models using the same dataset. The results of the study indicate that quantum machine learning classifiers perform better than classical machine learning classifiers in predicting heart disease. In addition, the study reveals that the bagging ensemble learning technique is effective in improving the prediction accuracy of quantum classifiers.
Cardiovascular disease remains one of the leading causes of death worldwide, making early and accurate risk prediction essential. Conventional machine learning models often struggle to handle complex medical data, resulting in limited accuracy and reduced interpretability. To address these challenges, this study presents a hybrid prediction framework that combines Quantum Machine Learning (QML) with ensemble learning techniques. Classical models such as Support Vector Classifier (SVC) and Artificial Neural Network (ANN) are used as baseline approaches. In addition, quantum models including QSVC, Variational Quantum Classifier (VQC), and Quantum Neural Network (QNN) are employed to capture complex non-linear patterns present in clinical data. To further enhance performance, a Bagging-QSVC ensemble model is proposed, improving robustness and overall classification accuracy. Interpretability is incorporated using SHAP, allowing the model’s predictions to be more transparent and easier to understand in a clinical context. Experimental results indicate that the proposed quantum ensemble achieves an accuracy of around 90%, outperforming both classical models and individual quantum models. Furthermore, a real-time user interface is developed, enabling clinicians to input patient data and receive immediate risk predictions. Overall, the system provides a reliable and interpretable solution suitable for practical clinical decision support.
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.
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.
Our research introduces a state-of-the-art bagging ensemble model that leverages Quantum Support Vector Classifiers (QSVC) to assess the risk of heart disease. Testing involved rigorous experimentation on the Cleveland data set for which QSVC has demonstrated better performance compared with benchmark classifiers and other quantum-inspired models as well, achieving an excellent accuracy of 90.16%. The boosted accuracy shows the competence of quantum machine learning in serving as a potential upgrade of predictive analytics within the healthcare industry. In addition to its impressive performance metrics, the model incorporates SHAP (SHapley Additive exPlanations) analysis, which significantly enhances its interpretability. By identifying and elucidating the utmost need of various features in this entire route, SHAP analysis makes the persons who are into medical practicing get to know more about the core of this model. This transparency is crucial in medical applications, where understanding the factors influencing a diagnosis can lead to better key decisions and sentiments of patients. Thus, Implications of this research extend beyond mere performance statistics. The research shows that quantum machine learning, and specifically QSVC, can revolutionize healthcare by providing interpretable and accurate predictive models. Over time, the technology will keep improving, and ultimately it will improve patient care and the quality of healthcare.