Key result
The stacked bidirectional LSTM and deep neural network model achieved 99% accuracy in detecting and classifying cardiac diseases from ECG data, outperforming state-of-the-art classifiers.
Why the study?
Early detection of cardiovascular disorders increases survival, creating a need for accurate and explainable machine learning predictive models.
Does a stacked bidirectional LSTM and deep neural network model improve the accuracy of cardiac disease detection and classification from ECGs compared to standard machine learning classifiers?
Does a stacked bidirectional LSTM and deep neural network model improve the accuracy of cardiac disease detection and classification from ECGs compared to standard machine learning classifiers?
A novel explainable AI model combining bidirectional LSTM and deep neural networks achieved 99% accuracy in detecting and classifying cardiac diseases from 12-lead ECGs, outperforming standard machine learning classifiers.
Cardiovascular disorders (heart diseases) are the most prevalent cause of death on a global scale. So early detection and classification increase the likelihood of survival. In the context of machine learning techniques, there is always a need for an accurate and explainable predictive model for detecting various diseases, such as cardiac disorders. The work carried out in this paper stacks bidirectional long short-term memory with deep learning to propose two models. The first model is used to detect cardiac disease with a binary label classification, while the second one classifies cardiac disease, which is a multi-label classification problem. Bidirectional LSTM is used as an approximate algorithm for feature extraction. Deep learning is used for classification purposes. The proposed models are trained and validated over the PTB-XL dataset. The performance of these models is evaluated and compared against state-of-the-art methods. The comparison shows the proposed model outperforms other methods in terms of accuracy, precision, f1-score, and recall. SHAP is used to make these models explainable, which in turn helps to annotate different diseases on the ECG report.
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Lipsa et al. (2025) studied Cardiovascular disease (n=18,885). BLSTM-DNN (Bidirectional LSTM and Deep Neural Network) vs. State-of-the-art machine learning classifiers (DNN, CNN, LSTM, DNN-SVM, DNN-RF, CNN-SVM) was evaluated on Classification accuracy. The stacked bidirectional LSTM and deep neural network model achieved 99% accuracy in detecting and classifying cardiac diseases from ECG data, outperforming state-of-the-art classifiers.
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