부산대학교 도서관

Better patient outcomes and prompt care depend on early detection of heart attacks. In this current work, we use the infamous MIT-BIH Arrhythmia Dataset, a reference resource for cardiac abnormality recognition, to train and validate our models. We propose an improved method for heart attack diagnosis and detection which includes a CNN model that is optimized with the help of the Grey Wolf Optimization (GWO) technique. This novel integration aims to enhance feature extraction and classification processes by tackling the drawbacks of traditional deep learning methods. A comprehensive testing process has been conducted on the proposed CNN-GWO model versus well-known deep learning models, such as Artificial Neural Networks also known as ANN, as well as many state-of-the-art machine learning models used for heart attack detection such as SVM, Decision Tree, and other ensemble learning models such as XG Boost. Our findings demonstrate a significant increase in detection accuracy and efficiency when comparing the CNN-GWO model to its rivals. This work not only demonstrates the effectiveness of combining CNN with GWO, but also demonstrates how well the proposed model performs in detecting heart attacks. This work contributes to the advancement of medical diagnostic technology by offering a more reliable and accurate diagnostic instrument. It also opens new options for algorithmic advancements in the future that forecast heart attacks.