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Quantum Support Vector Machines for Early Heart Disease Prediction

Pavan Nikhil Swamy Godavari, Bala Kameswari Jayanti, Dipanshi Pandey, Manojee K S

Published Apr 2, 2026• 0 citations
Publisher
DOI
10.5281/zenodo.19382188

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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.
Quantum Computing (QC) works based on the principle of quantum mechanics, which is different from traditional computers. Heart disease remains the leading cause of mortality worldwide and the development of advanced predictive models for early diagnosis is necessary. In this paper, the Support Vector Machine (SVM), Quantum Support Vector Machine (QSVM), and Variational Quantum Classifier (VQC) are used to predict heart disease. Among these algorithms, QSVM and VQC are used for heart disease prediction using QC principles. The SVM and QSVM achieves the accuracy of 80% and 69% and VQC achieves the highest accuracy of 86% with ZFeatureMap and RealAmplitude in 100 iterations. The FeatureMap and Ansatz are used for turning data into quantum states. The VQC employs a parameterized quantum circuit to minimize the classification error and optimization is done using gradient-free COBYLA. In addition to ZFeatureMap, other FeatureMaps like ZZFeatureMap and PauliFeatureMap are also performed in VQC.
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%.