Journal of Theoretical and Applied Information Technology
A QUANTUM CLASSICAL DEEP LEARNING FRAMEWORK FOR EARLY DETECTION AND TRIAGE OF CARDIOVASCULAR DISEASES USING MEDICAL IMAGING
DR SELVANI DEEPTHI KAVILA, P. HEMA VENKATA RAMANA, SELVA MALAR N, CHILAKALAPUDI MALATHI, DR DOKKU DURGA BHAVANI, DORABABU SUDARSA, N. SRIKANTH, MYLAVARAPU KALYAN RAM
Early and accurate recognition of cardiovascular disease (CVD) using medical imaging is essential for timely clinical intervention and management. The objective of this study was to continue developing a hybrid quantum-classical deep learning system for the early detection and automatic triage of cardiovascular diseases using cardiac imaging information. The proposed method consists of a combination of efficient feature extraction via a lightweight classical convolutional neural network and a quantum-illness feature transformation and decision-refinement process implemented as a variational quantum circuit. A clinically informed triage module was added to rate patients into three risk categories (low-, moderate-, and high-risk). Experimental evaluation on a public cardiac magnetic resonance imaging (MRI) dataset revealed that the proposed framework achieved 94.5% classification accuracy and an area under the curve (AUC) of 0.96, with sensitivity exceeding that of state-of-the-art classical deep learning models. The results show that quantum-enhanced feature representations provide better class separability, robustness to Imaging noise, and improved learning efficiency. The results suggest that one way to overcome the limitations of conventional deep models is the hybrid quantum-classical learning approach, which is also compatible with near-term quantum hardware. This work raises the question of whether quantum-assisted artificial intelligence can enable scalable, real-time cardiovascular decision support systems and promote next-generation intelligent healthcare systems.
Early and accurate recognition of cardiovascular disease (CVD) using medical imaging is essential for timely clinical intervention and management. The objective of this study was to continue developing a hybrid quantum-classical deep learning system for the early detection and automatic triage of cardiovascular diseases using cardiac imaging information. The proposed method consists of a combination of efficient feature extraction via a lightweight classical convolutional neural network and a quantum-illness feature transformation and decision-refinement process implemented as a variational quantum circuit. A clinically informed triage module was added to rate patients into three risk categories (low-, moderate-, and high-risk). Experimental evaluation on a public cardiac magnetic resonance imaging (MRI) dataset revealed that the proposed framework achieved 94.5% classification accuracy and an area under the curve (AUC) of 0.96, with sensitivity exceeding that of state-of-the-art classical deep learning models. The results show that quantum-enhanced feature representations provide better class separability, robustness to Imaging noise, and improved learning efficiency. The results suggest that one way to overcome the limitations of conventional deep models is the hybrid quantum-classical learning approach, which is also compatible with near-term quantum hardware. This work raises the question of whether quantum-assisted artificial intelligence can enable scalable, real-time cardiovascular decision support systems and promote next-generation intelligent healthcare systems.
Cardiovascular disease has become a major public health issue as the leading cause of death. Timely and accurate detection of heart disease is critical to prevent additional harm to patients. Recent studies show that the implementation of datadriven approaches in medicine, including machine learning (ML) and deep learning (DL), significantly improves the speed and accuracy of heart disease diagnosis. This study proposes a hybrid deep learning model that integrates traditional convolutional neural networks (CNNs) with quantum computing to improve the accuracy of the prediction of cardiovascular diseases. The model uses a parameterized quantum circuit (PQC) to encode patient data, including ECG signals and cardiac images, into quantum states to extract quantum features. Quantum gates modify quantum states to extract complex and high-dimensional features that may be beyond the capabilities of conventional algorithms. A CNN uses convolutional filters to detect localized patterns, including arrhythmias and structural heart abnormalities, to diagnose cardiovascular disease. The CNN then receives the output from the PQC. Integrating quantum feature extraction with conventional deep learning improves prediction accuracy and robustness, facilitating the analysis of high-dimensional and spatially dependent data. The ability of the hybrid model to identify cardiovascular diseases in their early stages may enable faster and more accurate diagnostic results, making it an innovative tool for personalized medicine.
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.
Cardiovascular diseases (CVDs) continue to be the leading global cause of death, requiring innovative approaches for early detection and accurate prediction. This study explores the transformative potential of quantum-enhanced machine learning (queml) models in cardiovascular disease prediction, addressing the limitations of conventional diagnostic approaches. Using extensive datasets including the Kaggle Heart Disease Dataset of 1,190 patient records and the Cleveland/State Registry datasets, the quantum-enhanced models show superior performance over traditional machine learning approaches, with QCVNET achieving 91.73% accuracy and queml showing a 0.6% improvement in accuracy, while reducing training time from 862.5 µs to 670 µs per cycle. Key physiological parameters including diabetes indicators, LDL cholesterol, HDL cholesterol, and triglycerides were incorporated as predictive features. The IQCNN framework achieved 91% classification accuracy at a learning rate of 70%, significantly outperforming the Bi-LSTM and CNN approaches. Comparative analysis revealed exceptional AUC scores of 96.43% and 97.78% for the hybrid models, establishing quantum-enhanced machine learning as a highly effective solution for cardiovascular risk assessment and early detection systems.
This research presents a novel quantum-hybrid machine learning framework for heart disease classification that integrates Quantum Neural Networks with variational circuits and Quantum Support Vector Machines featuring custom quantum kernels alongside classical ensemble methods. The methodology employs advanced feature engineering, multi-strategy missing value imputation, and combined feature selection techniques on an enhanced dataset of 2,000 cardiovascular cases with 17 engineered features. The quantum components utilize 8-10 qubits across 5-6 variational layers with quantum interference effects for enhanced pattern recognition capabilities. Through weighted soft voting ensemble strategy and dynamic threshold optimization, the quantum-hybrid system achieved exceptional performance metrics including 96.2% accuracy, 95.8% F1-score, 94.7% precision, and 96.9% recall, significantly exceeding the 95% accuracy target. The quantum models demonstrated measurable advantages over classical approaches, with the Quantum Neural Network reaching 92.4% accuracy and Quantum SVM achieving 90.8% accuracy independently. The hybrid ensemble showed 4.3 percentage points improvement over the best individual classical model and 2.7 percentage points over standalone quantum implementations. This quantum-hybrid approach establishes superior diagnostic capabilities for cardiovascular disease prediction, particularly in minimizing false negatives critical for patient safety, positioning quantum-enhanced machine learning as a viable solution for next-generation medical diagnostic systems with clinically relevant accuracy improvements for healthcare applications.
Quantum-Enhanced Machine Learning (QEML) is a new computational paradigm that introduces the concept of quantum mechanics with the latest artificial intelligence algorithms to enhance the performance of the diagnostic process in the field of medical imaging. The early identification of diseases is crucial in mortality reduction and improving patient outcome, especially in capacities dealing with such diseases as cancer, neurological diseases, heart diseases, and infections of the lungs. Although conventional deep learning architectures, in particular, Convolutional Neural Networks (CNNs) have been highly successful in processing complex medical images such as MRI scans, CT images, X-rays, and histopathological data, they can be computationally expensive, need large data sets, and suffer in optimization of high dimensional feature spaces. The limitations that QEML tries to overcome are based on the quantum mechanical principles of superposition, entanglement and interference to facilitate better data representation and processing. The suggested structure is based on the classical deep learning architectures and variational quantum circuits together, to create a hybrid quantum-classical system, which improves features extraction and classification levels. Under this method, the classical image characteristics are coded into quantum states and processed in high-dimensional Hilbert spaces, which can enhance the separability of subtle disease patterns, which would have otherwise been challenging to obtain with conventional techniques. The objective of this hybrid system is to enhance the critical diagnostic measures like sensitivity and specificity without affecting the interpretability of such measures to be used in the clinical context. The proposed framework is also assessed in terms of its feasibility, its computational performance and scalability to the capabilities of present-day Noisy Intermediate-Scale Quantum (NISQ) devices. The model effectiveness is evaluated using objective evaluation metrics, which, in a way, guarantee the feasibility of the model application within a real-life healthcare context. On balance, quantum computing and medical image analysis integration is an exciting field of work of the future intelligent healthcare systems and has a potential of providing more efficient, accurate, and scalable diagnostic solutions.
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.