Enhanced Cardiovascular Disease Classification: Optimizing LSTM-Based Model with Ant-Lion Algorithm for Improved Accuracy
Year: 2023
Type: article
Abstract: Cardiovascular diseases pose a significant global health challenge, emphasizing the need for improved techniques in early detection and diagnosis.This study focuses on enhancing the classification of cardiovascular diseases by optimizing an LSTM-based model using the Ant-lion algorithm.In order to achieve this, we utilize the Local Binary Patterns (LBP) technique for feature extraction, which captures important patterns in electrocardiography (ECG) data.The Ant-lion algorithm is then employed to optimize the LSTM model and improve its performance.To evaluate the proposed methodology, we selected the widely used MIT-BIH Arrhythmia Dataset.This dataset contains a variety of heart disease cases, enabling comprehensive testing and validation.In addition to accuracy, we assess various quantitative metrics such as precision, recall, and F1-score to provide a more comprehensive evaluation of the model's performance.This research contributes to the field of ECG classification by highlighting the potential of combining deep learning models with meta-heuristic algorithms.The findings validate the effectiveness of our approach on the MIT-BIH Arrhythmia Dataset, reinforcing the importance of further exploring such optimization techniques in cardiovascular disease diagnosis and management.
DOI: 10.18280/ria.370418
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4386834293