Elshewey Ahmed M, Abed Amira Hassan, Khafaga Doaa Sami, Alhussan Amel Ali, Eid Marwa M, El-Kenawy El-Sayed M
Department of Computer Science, Faculty of Computers and Information, Suez University, P.O.BOX:43221, Suez, Egypt.
Department of Information Systems, High Institution for Marketing, Commerce & Information Systems, Cairo, Egypt.
Sci Rep. 2025 Jan 8;15(1):1277. doi: 10.1038/s41598-024-83592-0.
Heart disease is a category of various conditions that affect the heart, which includes multiple diseases that influence its structure and operation. Such conditions may consist of coronary artery disease, which is characterized by the narrowing or clotting of the arteries that supply blood to the heart muscle, with the resulting threat of heart attacks. Heart rhythm disorders (arrhythmias), heart valve problems, congenital heart defects present at birth, and heart muscle disorders (cardiomyopathies) are other types of heart disease. The objective of this work is to introduce the Greylag Goose Optimization (GGO) algorithm, which seeks to improve the accuracy of heart disease classification. GGO algorithm's binary format is specifically intended to choose the most effective set of features that can improve classification accuracy when compared to six other binary optimization algorithms. The bGGO algorithm is the most effective optimization algorithm for selecting the optimal features to enhance classification accuracy. The classification phase utilizes many classifiers, the findings indicated that the Long Short-Term Memory (LSTM) emerged as the most effective classifier, achieving an accuracy rate of 91.79%. The hyperparameter of the LSTM model is tuned using GGO, and the outcome is compared to six alternative optimizers. The GGO with LSTM model obtained the highest performance, with an accuracy rate of 99.58%. The statistical analysis employed the Wilcoxon signed-rank test and ANOVA to assess the feature selection and classification outcomes. Furthermore, a set of visual representations of the results was provided to confirm the robustness and effectiveness of the proposed hybrid approach (GGO + LSTM).
心脏病是影响心脏的各类病症的统称，其中包括多种影响心脏结构和功能的疾病。这类病症可能包括冠状动脉疾病，其特征是为心肌供血的动脉变窄或形成血栓，进而引发心脏病发作的风险。心律失常、心脏瓣膜问题、先天性心脏缺陷以及心肌疾病（心肌病）是其他类型的心脏病。这项工作的目的是介绍灰雁优化（GGO）算法，该算法旨在提高心脏病分类的准确性。GGO算法的二进制形式专门用于选择最有效的特征集，与其他六种二进制优化算法相比，这些特征可以提高分类准确性。bGGO算法是选择最优特征以提高分类准确性的最有效优化算法。分类阶段使用了多种分类器，结果表明长短期记忆（LSTM）成为最有效的分类器，准确率达到91.79%。使用GGO对LSTM模型的超参数进行调整，并将结果与六种替代优化器进行比较。带有LSTM模型的GGO获得了最高性能，准确率为99.58%。统计分析采用威尔科克森符号秩检验和方差分析来评估特征选择和分类结果。此外，还提供了一组结果的可视化表示，以确认所提出的混合方法（GGO + LSTM）的稳健性和有效性。
