Quantum Machine Learning represents a promising approach to address complex computational challenges beyond classical computing capabilities. In this study, we investigate its application to healthcare, focusing specifically on the classification of cardiovascular diseases. Classical Machine Learning methods often struggle with high-dimensional data, resulting in limited accuracy and reduced generalization, especially in real-world medical scenarios. To overcome these issues, we propose a Hybrid Quantum-Neural Network that combines classical Autoencoders for dimensionality reduction with Quantum Neural Networks, facilitating effective data processing compatible with current NISQ hardware. Evaluated on the Cleveland dataset, our HQNN achieves state-of-the-art classification accuracy up to 90.98%, outperforming purely classical counterparts by capturing complex feature relationships in the quantum domain. We further demonstrate the effectiveness of the proposed HQNN model in scenarios characterized by limited training data and realistic noisy quantum environments, performing robustness analyses and simulations using IBM’s quantum hardware framework. These findings highlight the practical advantages of our Hybrid Quantum-Neural Network, underscoring its suitability and immediate applicability for real-world healthcare scenarios. The code is made publicly available.
Quantum Machine Learning represents a promising approach to address complex computational challenges beyond classical computing capabilities. In this study, we investigate its application to healthcare, focusing specifically on the classification of cardiovascular diseases. Classical Machine Learning methods often struggle with high-dimensional data, resulting in limited accuracy and reduced generalization, especially in real-world medical scenarios. To overcome these issues, we propose a Hybrid Quantum-Neural Network that combines classical Autoencoders for dimensionality reduction with Quantum Neural Networks, facilitating effective data processing compatible with current NISQ hardware. Evaluated on the Cleveland dataset, our HQNN achieves state-of-the-art classification accuracy up to 90.98%, outperforming purely classical counterparts by capturing complex feature relationships in the quantum domain. We further demonstrate the effectiveness of the proposed HQNN model in scenarios characterized by limited training data and realistic noisy quantum environments, performing robustness analyses and simulations using IBM’s quantum hardware framework. These findings highlight the practical advantages of our Hybrid Quantum-Neural Network, underscoring its suitability and immediate applicability for real-world healthcare scenarios. The code is made publicly available.
Heart disease is one of the major global causes of death, requiring early and precise detection for efficient treatment.With advancements in quantum computing, Variational Quantum Circuits (VQCs) have gained recognition as a valuable technique for improving learning models in medical diagnosis.This study further explores the application of VQC-based models for heart disease prediction, leveraging quantum computing to enhance classification accuracy.A hybrid quantum-classical model has been developed, which combines classical preprocessing for feature extraction from patient datasets with a VQC-based classifier for medical predictions.The quantum circuit using variational parameters is optimized to reduce the loss function and facilitate effective learning from complex medical data.This proposed approach is tested on benchmark heart disease datasets and evaluated against classical Machine Learning (ML) models, including Support Vector Machines (SVMs), Random Forests, and Neural Networks.The experimental results show that VQC-based models achieve high accuracy and outperform classical models, notably on high-dimensional, non-linearly separable data.The quantum advantage of VQCs lies in their ability to encode and process data in a huge Hilbert space.However, challenges such as noise in near-term quantum devices and scalability issues remain key.This research underscores the efficacy of VQCbased quantum machine learning in medical diagnosis, paving the way for future studies on quantum-enhanced healthcare solutions.Further advancements in quantum hardware and hybrid learning techniques could allow quantum-assisted heart disease prediction to become an effective clinical tool.
Quantum Neural Networks (QNNs) are a promising tool for addressing the limitations of their classical counterparts in many applications, including healthcare. However, most existing approaches rely on gradient-based optimization methods, which require a large number of quantum circuit calls and are affected by barren plateaus, limiting their applicability on current Noisy Intermediate-Scale Quantum (NISQ) devices. This work investigates Evolutionary Quantum Neural Networks (EQNNs) for heart disease classification using the Cleveland dataset. The proposed framework replaces gradient-based training with a Genetic Algorithm (GA), enabling parameter optimization without explicit gradient computation and reducing the dependence of training cost on the number of circuit parameters. Experimental results compare the GA with the ADAM optimizer across different circuit sizes and under both ideal and noisy execution conditions using a mock IBM backend. The results demonstrate that EQNNs achieve competitive classification performance while significantly reducing the number of required quantum circuit evaluations, highlighting their potential as a resource-efficient solution for medical diagnosis on NISQ devices.
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.
Cardiovascular diseases remain a leading global health challenge, driving the need for advanced, accurate diagnostic tools. While classical machine learning (ML) models like XGBoost have achieved significant success in medical diagnosis, they often face challenges related to accuracy plateaus and model explainability in complex, high-dimensional data spaces. Quantum machine learning (QML) emerges as a promising paradigm to potentially overcome these limitations by leveraging quantum computational advantages. This study aims to develop and evaluate a hybrid quantum-classical pipeline for the early detection of heart disease. We integrate robust data preprocessing—including missing value imputation, feature engineering, SMOTE for class balancing, and PCA for dimensionality reduction—with two classification approaches: a classical XGBoost model and a quantum Variational Quantum Classifier (VQC). Our results demonstrate that the XGBoost model achieves strong performance with an accuracy of 88.52% and an F1-score of 0.8852 on an imbalanced test set. While the VQC model did not surpass the classical baseline in the current noisy simulation environment, it shows theoretical promise. This work highlights the practical challenges of current QML and outlines a clear pathway for future research towards achieving a quantum advantage in clinical decision support systems.
Accurate classification of microscopic blood cells is still a critical task in medical image analysis, where subtle variations and limited data can challenge conventional deep learning models. As such, we investigate in this work the potential of Hybrid Quantum-Classical Neural Networks (HQNNs) to enhance feature representation and improve classification performance in this domain. We propose a modular architecture combining a pre-trained ResNet-50 backbone with a low-dimensional latent bottleneck and a variational quantum circuit, enabling a direct comparison between quantum-enhanced and purely classical transformation mechanisms. To isolate the contribution of the quantum component, we evaluate three architectures: a HQNN model, a Classical Matched Model with an additional nonlinear transformation layer of comparable capacity, and a baseline model without an intermediate transformation stage. Experiments conducted on two publicly available blood cell datasets, namely the Blood Cell Images dataset and the PBC dataset, demonstrate that HQNNs consistently achieve superior or more balanced performance across evaluation metrics. In the Blood Cell Images Dataset, the proposed approach improves macro F1-score by up to 3.7% compared to classical baselines, while improving the F1-score from 98.54% to 98.69% in the more challenging 8-class scenario with near-saturated performance. Additional evaluation on IBM quantum hardware shows that the model remains robust under noise, with only a modest performance degradation relative to simulated results. These results indicate that quantum feature transformations can enhance discriminative representations, particularly in challenging classification scenarios, and highlight the practical potential of HQNN models for medical imaging tasks.
The cardiovascular and neurological conditions are one of the predominant causes of death and disability at a global scale, thus the need to develop smart healthcare systems to detect diseases at their initial stages or at all. The limitation of the conventional artificial intelligence-based and machine learning-based healthcare models are usually rife with high levels of computation complexity, low levels of prediction capabilities, poor scaling, and ineffective processing of complex healthcare data. The proposed solution to these problems is a Quantum Neural Network-Based Healthcare Analytics Framework that could be used to detect cardiovascular and neurological disorders at their initial stages. This framework is a combination of quantum computing and neural network models to improve diagnostic abilities in the field of healthcare analytics in disease classification, predictive accuracy, and computational efficiency. Experimental evaluation was done using healthcare datasets that comprised cardiovascular and neurological records of patients after being processed through preprocessing methods such as normalization, feature extraction and data balancing. Classification performance measures were used to assess the performance of the proposed model and these measures include Accuracy, Precision, Recall, Specificity, F1-Score, and AUC-ROC. The experimental findings showed that the suggested quantum neural network model worked much better than the traditional machine learning and deep learning models with regard to disease prediction accuracy, classification reliability, and the ability to detect disease early. Additionally, the framework had better sensitivity and lower false prediction, thus improving clinical decision-making support. The study helps expand the intelligent quantum healthcare analytics system to the next generation of medical diagnosis and predictive healthcare uses.