Heart disease prediction has become an important area of research in the field of healthcare due to its global health impact. Early detection of heart disease can significantly reduce mortality rates and improve patient outcomes. This study utilizes a publicly available heart disease dataset with 14 attributes, including age, sex, chest pain type, resting blood pressure, serum cholesterol, and other clinical measurements, to predict the presence of heart disease. The dataset contains 76 records, where the target variable indicates the presence or absence of heart disease (0 = no disease, 1 = disease). This work proposes the application of advanced machine learning techniques, specifically the Variational Quantum Classifier (VQC) and Quantum Support Vector Machine (QSVM), to predict heart disease. The VQC leverages the power of quantum computing for classification tasks, while QSVM applies quantum principles to support vector machines, offering enhanced accuracy and performance. These quantum-based algorithms are expected to provide a promising alternative to traditional machine learning models, especially in terms of handling complex, high-dimensional datasets with enhanced computational efficiency. By comparing the performance of VQC and QSVM with classical machine learning methods, this research aims to demonstrate the potential of quantum algorithms in medical predictions, particularly in the early detection of heart disease, thereby contributing to more effective healthcare management.

Keywords

Heart disease prediction, quantum computing, Variational Quantum Classifier (VQC), Quantum Support Vector Machine (QSVM), machine learning, healthcare management, early detection, high-dimensional datasets, computational efficiency, medical predictions.