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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.

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Summary

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.

Keywords:
Feature selectionHeart disease classificationLSTMOptimizationbGGO

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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.