This study proposes a novel approach to heart disease prediction using quantum neural networks, which addresses the cardiovascular disease as the world's biggest cause of mortality, a global challenge. By leveraging quantum computing capabilities, our system aims to outperform conventional decision support systems in identifying early indicators of heart disease before clinical symptoms appear. The research explores how quantum neural networks, which are capable of handling multiple quantum states simultaneously, can more effectively handle complicated medical data than traditional methods. Our approach combines deep learning techniques that eliminate the need for manual feature extraction, thereby improving both resource and time efficiency. The method analyses important risk factors, including diabetes, LDL cholesterol, HDL levels, and triglycerides, to predict disease likelihood using quantum variation algorithms and random forest regression. This interdisciplinary Research advances the expanding field of artificial intelligence powered cardiology, promising more accurate and early diagnoses that are essential for effective treatment. The proposed system has significant potential for medical applications, ultimately aiming to reduce mortality rates and improve patient outcomes through advanced quantum computing technologies.
This study proposes a novel approach to heart disease prediction using quantum neural networks, which addresses the cardiovascular disease as the world's biggest cause of mortality, a global challenge. By leveraging quantum computing capabilities, our system aims to outperform conventional decision support systems in identifying early indicators of heart disease before clinical symptoms appear. The research explores how quantum neural networks, which are capable of handling multiple quantum states simultaneously, can more effectively handle complicated medical data than traditional methods. Our approach combines deep learning techniques that eliminate the need for manual feature extraction, thereby improving both resource and time efficiency. The method analyses important risk factors, including diabetes, LDL cholesterol, HDL levels, and triglycerides, to predict disease likelihood using quantum variation algorithms and random forest regression. This interdisciplinary Research advances the expanding field of artificial intelligence powered cardiology, promising more accurate and early diagnoses that are essential for effective treatment. The proposed system has significant potential for medical applications, ultimately aiming to reduce mortality rates and improve patient outcomes through advanced quantum computing technologies.
Cardiovascular disease is one of the most common reasons of increases the global death rate, suggesting initial detection and timely treatment crucial. While traditional machine learning models have been effective in predicting heart disease, Quantum Computing (QC) introduces new possibilities for improving accuracy and processing speed. In this regard the application of Quantum Machine Learning (QML) approaches-categorically, Quantum Neural Networks (QNN) and Quantum Support Vector Classification (QSVC)-to predict heart disease using key health indicators such as present-age, levels of cholesterol, Blood Pressure (BP), and lifestyle choices. QNN is utilized to recognize complex patterns in patient data, leveraging deep learning to understand intricate connections between various risk factors. Meanwhile, QSVC takes advantage of QC ability to compute large amounts of information at once, making classification faster and more precise. Both models are trained and tested on a heart disease dataset and compared with traditional machine learning methods to assess their accuracy, efficiency, and scalability. The results show that while quantum and classical models achieve similar accuracy, quantum computing significantly reduces processing time, making it highly efficient for analyzing large datasets. This study highlights the strength of QC in transforming medical diagnostics, offering faster and more reliable predictive models for early heart disease detection. By integrating quantum algorithms into healthcare, this research contributes to the advancement of AI-driven medical solutions, resulting in more efficient and individual patient care.
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.
Cardiac risk prediction is critical for the early detection and prevention of cardiovascular diseases, a leading global cause of mortality. In response to the growing volume and complexity of healthcare data, there has been increasing reliance on computational approaches to enhance clinical decision-making and improve early detection of cardiac risks. Although classical machine learning techniques have demonstrated strong performance in cardiovascular disease prediction, their efficiency and scalability are increasingly challenged by high-dimensional and large-scale medical datasets. Emerging advances in quantum computing have introduced quantum machine learning (QML) as a promising alternative, offering novel computational paradigms with the potential to outperform classical methods in terms of speed and problem-solving capability. This review analyzed twelve studies, evaluating data types, quantum architecture, performance metrics, and comparative efficacy against classical machine learning models. Our findings indicate that QNNs show promise for enhanced predictive accuracy and computational efficiency. However, significant challenges in scalability, noise resilience, and clinical integration persist. The translation of quantum advantage into clinical practice necessitates further validation on large-scale with diverse datasets.
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.
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.
Heart diseases are encountered as a leading root for higher death cases around the globe. However, the death rate can be reduced by taking proper treatment procedures and medical inspections. The early identification of heart diseases poses huge challenges to the physicians due to the complexity of the root causes. The emergence of intelligent decision-making support systems aids in predicting the diseases at the earliest. The recent innovations handle a huge amount of earlier data and produce accurate results as expected. The introduction of quantum computing-based machine learning approaches produces outstanding results in prediction and classification. The proposed research work aims to improve the heart disease prediction rate by incorporating Quantum Support Vector Machine Algorithms (QSVM). The outcomes of this research work shows the improved results in terms of prediction accuracy. Further, the additional evaluation metrics state the significance of the proposed work.