Key result
EBiLSTM-AEOA deep learning model boosts cardiovascular disease classification accuracy by ~10% over standard methods.
Why the study?
Cardiovascular disease classification is crucial because it remains a leading cause of death worldwide and often progresses without clear initial signs.
Effect estimate: 10.34% improvement in accuracy and 8.65% in sensitivity
The proposed EBiLSTM-AEOA deep learning framework improves cardiovascular disease classification accuracy by 10.34% and sensitivity by 8.65% compared to existing methods.
Should not yet change clinical practice; leaves open prospective validation of EBiLSTM-AEOA for CVD classification.
Classifying Cardiovascular Diseases (CVDs) describes a crucial task in modern healthcare, as it remains a significant reason of death worldwide and often progresses without clear initial signs. Hence, this article performs the Cardiovascular Disease Classification (CVDC) model using intelligent deep learning framework. The dataset is first collected from standard benchmark sources like Kaggle named to be Automated Cardiac Diagnosis Challenge (ACDC) dataset. The pre-processing is next followed using the Synthetic Minority Oversampling Technique (SMOTE) approach. Next, the segmentation takes place with the help of 3D U-Net technique. The features are further extracted by the Wavelet Scattering Transform (WST) method. Finally, the classification of the proposed CVDC model is accomplished by the novel Enhanced Bidirectional Long Short Term Memory (EBiLSTM) model. The parameter tuning in BiLSTM is performed by nature inspired optimization algorithm known as Artificial Eagle Optimization Algorithm (AEOA). Accuracy maximization is considered to be the fitness function behind the entire procedure. The proposed EBiLSTM-AEOA is 10.34% and 8.65% better than the remaining considered methods for the suggested CVDC model in terms of accuracy and sensitivity, respectively.
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Sekar et al. (2026) studied Cardiovascular Diseases. EBiLSTM-AEOA model vs. Other considered machine learning methods was evaluated on Classification accuracy and sensitivity (10.34% improvement in accuracy and 8.65% in sensitivity). The proposed EBiLSTM-AEOA deep learning model improved cardiovascular disease classification accuracy by 10.34% and sensitivity by 8.65% compared to other considered methods.
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