Enhancing heart disease classification based on greylag goose optimization algorithm and long short-term memory
Abstract 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...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-024-83592-0 |
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| author | Ahmed M. Elshewey Amira Hassan Abed Doaa Sami Khafaga Amel Ali Alhussan Marwa M. Eid El-Sayed M. El-kenawy |
| author_facet | Ahmed M. Elshewey Amira Hassan Abed Doaa Sami Khafaga Amel Ali Alhussan Marwa M. Eid El-Sayed M. El-kenawy |
| author_sort | Ahmed M. Elshewey |
| collection | DOAJ |
| description | Abstract 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). |
| format | Article |
| id | doaj-art-dc5528d4ece64603afdd352bf8219953 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-dc5528d4ece64603afdd352bf82199532025-01-12T12:20:15ZengNature PortfolioScientific Reports2045-23222025-01-0115112110.1038/s41598-024-83592-0Enhancing heart disease classification based on greylag goose optimization algorithm and long short-term memoryAhmed M. Elshewey0Amira Hassan Abed1Doaa Sami Khafaga2Amel Ali Alhussan3Marwa M. Eid4El-Sayed M. El-kenawy5Department of Computer Science, Faculty of Computers and Information, Suez UniversityDepartment of Information Systems, High Institution for Marketing, Commerce & Information SystemsDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityFaculty of Artificial Intelligence, Delta University for Science and TechnologyDepartment of Communications and Electronics, Delta Higher Institute of Engineering and TechnologyAbstract 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).https://doi.org/10.1038/s41598-024-83592-0bGGOOptimizationHeart disease classificationFeature selectionLSTM |
| spellingShingle | Ahmed M. Elshewey Amira Hassan Abed Doaa Sami Khafaga Amel Ali Alhussan Marwa M. Eid El-Sayed M. El-kenawy Enhancing heart disease classification based on greylag goose optimization algorithm and long short-term memory Scientific Reports bGGO Optimization Heart disease classification Feature selection LSTM |
| title | Enhancing heart disease classification based on greylag goose optimization algorithm and long short-term memory |
| title_full | Enhancing heart disease classification based on greylag goose optimization algorithm and long short-term memory |
| title_fullStr | Enhancing heart disease classification based on greylag goose optimization algorithm and long short-term memory |
| title_full_unstemmed | Enhancing heart disease classification based on greylag goose optimization algorithm and long short-term memory |
| title_short | Enhancing heart disease classification based on greylag goose optimization algorithm and long short-term memory |
| title_sort | enhancing heart disease classification based on greylag goose optimization algorithm and long short term memory |
| topic | bGGO Optimization Heart disease classification Feature selection LSTM |
| url | https://doi.org/10.1038/s41598-024-83592-0 |
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