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Enhancing heart disease classification based on greylag goose optimization algorithm and long short-term memory
Ahmed M Elshewey1, Amira Hassan Abed2, Doaa Sami Khafaga3
1Department of Computer Science, Faculty of Computers and Information, Suez University, P.O.BOX:43221, Suez, Egypt. ahmed.elshewey@fci.suezuni.edu.eg.
Insights
This study introduces the Greylag Goose Optimization (GGO) algorithm for heart disease classification. The GGO-tuned LSTM model achieved 99.58% accuracy, significantly improving heart disease detection.
Area of Science:
- Cardiology
- Computer Science
- Artificial Intelligence
Background:
- Heart disease encompasses various conditions affecting heart structure and function, including coronary artery disease, arrhythmias, and cardiomyopathies.
- Accurate heart disease classification is crucial for timely diagnosis and treatment, yet remains a challenge.
Purpose of the Study:
- To introduce the Greylag Goose Optimization (GGO) algorithm for enhancing heart disease classification accuracy.
- To evaluate the effectiveness of the binary GGO (bGGO) algorithm in selecting optimal features for improved classification.
- To compare the performance of GGO-tuned Long Short-Term Memory (LSTM) models against other optimizers.
Main Methods:
- Development and application of the binary Greylag Goose Optimization (bGGO) algorithm for feature selection.
- Utilizing Long Short-Term Memory (LSTM) networks as the primary classifier.
- Tuning LSTM hyperparameters using the GGO algorithm and comparing with six other optimization techniques.
- Employing statistical analyses, including Wilcoxon signed-rank test and ANOVA, for outcome assessment.
Main Results:
- The bGGO algorithm demonstrated superior feature selection capabilities compared to six other binary optimization algorithms.
- The Long Short-Term Memory (LSTM) classifier achieved an initial accuracy of 91.79% for heart disease classification.
- The hybrid GGO + LSTM model achieved a significantly higher accuracy rate of 99.58% after hyperparameter tuning.
- Statistical analyses and visual representations confirmed the robustness and effectiveness of the proposed GGO + LSTM approach.
Conclusions:
- The Greylag Goose Optimization algorithm, particularly its binary variant, is highly effective for feature selection in heart disease classification.
- The GGO algorithm significantly enhances the performance of Long Short-Term Memory models, leading to superior heart disease detection accuracy.
- The proposed hybrid GGO + LSTM approach represents a robust and effective method for improving cardiovascular disease diagnosis.
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).
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