<![CDATA[Heart attact is one of the leading causes of death worldwide, with mortality increasing due to delayed diagnosis and limited facilities in some regions. Early detection during the crucial first hour after symptoms appear (the golden hour) is crucial for reducing mortality and improving patient prognosis. This study aims to evaluate the performance of the Quantum Neural Network (QNN) by implementing the Variational Quantum Classifier (VQC) model using two types of feature maps: ZZ Feature Map and Angle Encoding, for heart attack detection classification using datasets from Kaggle. The research process includes dataset collection, Exploratory Data Analysis (EDA), data preprocessing and splitting, model building using ZZ Feature Map and Angle Encoding, ending with model performance evaluation. The results showed that VQC using ZZ Feature Map achieved an accuracy of 52.27% with a confusion matrix showing suboptimal predictions and relatively low precision, recall, and F1-score values. Meanwhile, VQC using Angle Encoding achieves an accuracy of 68.18% with a confusion matrix that shows a higher number of correct predictions and better precision, recall, and F1-score results compared to VQC using ZZ Feature Map. Evaluation using a confusion matrix shows that the model with Angle Encoding can minimize the prediction error. However, the accuracy achieved is not yet optimal for direct clinical implementation. These findings underscore the need for further development to enhance model performance, while serving as a promising first step toward establishing a foundation for developing more optimal QNN methods in the future.]]>
