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Internet Technology Letters

Quantum Computing Method for Prediction of the Cardiovascular Disease

M. Gayathri, R. Thilagavathy, M. Pushpalatha, Raghvendra Kumar, Kusum Yadav, Lulwah M. Alkwai

Published Oct 14, 2025• 0 citations
Publisher

Abstract

ABSTRACT Cardiovascular complications pertain to damages done to the myocardium and the arteries, as well as other parts of the circulatory system. Some important ischemic heart disease (IHD) risk factors include some health assessment metrics, age, and gender. In the prediction of cardiac diseases and in other tough decisions involving the analytics of large volumes of medical data, quantum computation techniques have become a staple in the healthcare field. Studies recently focus on the application of such procedures in quantum learning (QL) algorithms. For heart disease diagnosis, this work came up with a set of QL algorithms, including optimized quantum qubit vector learning (OQQVL) and quantum stacked convolutional neural networks (QSCNN), which we consider computationally light. By incorporating strong quantum learning based pre‐processing and feature clustering techniques like quantum K means, the proposed models' prediction reliability along with accuracy is sharpened whilst ensuring robustness. A trade‐off between the proposed and more sophisticated recently published models is done using available performance metrics. Thus, using the proposed techniques, the models achieved the highest accuracies of 99.1% and 99.5%, respectively. Moreover, the sophisticated structure provided reasonable execution times, making them suitable for real‐time healthcare use.

DOI
10.1002/itl2.70059

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ABSTRACT Cardiovascular complications pertain to damages done to the myocardium and the arteries, as well as other parts of the circulatory system. Some important ischemic heart disease (IHD) risk factors include some health assessment metrics, age, and gender. In the prediction of cardiac diseases and in other tough decisions involving the analytics of large volumes of medical data, quantum computation techniques have become a staple in the healthcare field. Studies recently focus on the application of such procedures in quantum learning (QL) algorithms. For heart disease diagnosis, this work came up with a set of QL algorithms, including optimized quantum qubit vector learning (OQQVL) and quantum stacked convolutional neural networks (QSCNN), which we consider computationally light. By incorporating strong quantum learning based pre‐processing and feature clustering techniques like quantum K means, the proposed models' prediction reliability along with accuracy is sharpened whilst ensuring robustness. A trade‐off between the proposed and more sophisticated recently published models is done using available performance metrics. Thus, using the proposed techniques, the models achieved the highest accuracies of 99.1% and 99.5%, respectively. Moreover, the sophisticated structure provided reasonable execution times, making them suitable for real‐time healthcare use.
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.
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.
The aim of this study is to enhance the accuracy and reduce the time complexity of predicting cardiac illnesses by utilizing patient vital signs exclusively. The importance of timely treatment in critical situations where quick decisions are necessary is emphasized here. Precise predictions have the potential to prevent health deterioration and even save lives. By analyzing vast datasets (images, texts etc.) and detecting subtle patterns, quantum machine learning algorithms (QML) can offer more accurate results in a shorter period compared to classical algorithms. The feature set used here is a novel one to be used for the quick detection and early prediction of cardiovascular diseases, and also a convenient one for this task. To predict heart diseases or abnormalities, both classical and quantum machine learning techniques have been employed in this paper. We have developed four models based on Support Vector Machine (SVM), Neural Network (NN), Quantum Support Vector Machine (QSVM), and Quantum Neural Network (QNN) and compared their performance on a balanced sample of a vast dataset for heart diseases prediction. After comparing the models' performance, we found that Quantum Support Vector Machine (QSVM) performed best with an accuracy of 75% and to-fold Cross-validation score of 80%.
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.
Quantum computing has emerged as a powerful tool for solving complex problems in various fields. Personalized medicine, tailoring medical treatment to patients based on their genetic and health data, is one area where predictive analytics can be useful. This work explores the application of quantum algorithms for predictive analytics, specifically in the context of predicting outcomes of cardiovascular disease based on patient data. This research is focused on the quantum-based predictive models for the case study of cardiovascular disease. The models are based on Quantum Support Vector Machines, Quantum Neural Networks, and Variational Quantum Eigensolver algorithms. The software implementation is based on the Python programming language, including an integrated quantum algorithm. A dataset of cardiovascular disease from an online platform is used to train and evaluate the models.
Heart disease continues to be among the top causes of death globally, and thus precise and timely detection mechanisms are necessary for successful clinical intervention. This chapter introduces a sophisticated predictive model integrating quantum-enhanced feature extraction with classical and quantum machine learning (QML) models to enhance prediction accuracy and reliability of heart disease. Quantum models like quantum random forest (QRF), quantum k-nearest neighbor (QKNN), and quantum gradient boosting classifier (QGBC) are used in conjunction with traditional models like XGBoost and SVM to investigate hybrid computational synergies. The method utilizes quantum feature mapping and entanglement-based encoding to identify intricate nonlinear patterns present in cardiovascular data. A thorough performance comparison based on the UCI Heart Disease dataset shows that quantum-augmented models exhibit better predictive accuracy, precision, and F1-scores than conventional models. The findings support the potential of quantum computing to speed up healthcare analytics and make more accurate disease diagnosis possible. This work discusses the pioneering merger of quantum computing and machine learning as a revolutionary strategy for data-driven intelligent medical decision support systems.