Advancing Early Detection of Congestive Heart Failure Using BiLSTM Networks: A Robust Clinical Framework

Congestive Heart Failure (CHF) is a prevalent cardiovascular disorder that requires early detection for effective management. This paper presents a comprehensive framework leveraging Bidirectional Long Short-Term Memory (BiLSTM) networks for the early detection of CHF using Electrocardiogram (ECG) data. The methodology involves preprocessing ECG signals, extracting relevant features, normalizing the data, and training a BiLSTM model. Our experimental results demonstrate high accuracy and robustness, highlighting the potential of BiLSTM networks in clinical applications for CHF prediction. The proposed method achieves an accuracy of 98.50%, sensitivity of 98.36%, and specificity of 98.57%, outperforming existing techniques. The model's ability to capture temporal dependencies in ECG signals through bidirectional learning contributes to its superior performance in identifying early signs of CHF. The findings suggest that BiLSTM networks hold promise for enhancing CHF detection accuracy, which could significantly impact healthcare outcomes by enabling early intervention and reducing medical costs.