article · Scientific Reports
Heart disease encompasses diverse conditions that affect cardiac structure and function, including coronary artery disease, arrhythmias, valve problems, congenital defects, and cardiomyopathies. Improving the computational detection of these conditions is critical for timely care. This study introduces a classification framework based on the Greylag Goose Optimization algorithm coupled with a Long Short-Term Memory neural network. A binary version of the optimization algorithm selects the most relevant clinical features, outperforming six other binary optimization methods. In classification tests across multiple models, the baseline Long Short-Term Memory network achieved an initial accuracy of 91.79 percent. Tuning the network hyperparameters using the Greylag Goose Optimization algorithm raised classification accuracy to 99.58 percent, exceeding the results of six alternative optimizers. Statistical analyses, including analysis of variance and the Wilcoxon signed-rank test, confirmed the robustness of the combined feature selection and classification pipeline.
Heart diseases represent a broad and dangerous group of conditions where accurate detection is vital. Machine learning models often suffer from poor accuracy when processing complex medical data with redundant variables. By combining a novel biological optimization technique with neural networks, this approach demonstrates that automated systems can filter diagnostic data effectively and achieve near-perfect classification performance on heart disease datasets.
This methodology could support software developers and clinical diagnostics providers building automated decision-support tools for cardiologists. Because the abstract reports algorithmic benchmarks rather than clinical trials or integrations with hospital electronic health records, the technology is currently at an applied, computational research stage and requires clinical validation before commercial deployment.
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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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DOI: 10.1038/s41598-024-83592-0
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