An Enhanced Attention Mechanism with Long Short-Term Memory (EALSTM) model achieved 97.65% accuracy, 99.21% sensitivity, and 98.04% specificity for automatic CKD classification using ECG signals.
Does an Enhanced Attention Mechanism with LSTM using ECG signals accurately classify chronic kidney disease?
An enhanced deep learning model using ECG signals can accurately identify chronic kidney disease with over 97% accuracy.
Globally, undiagnosed chronic kidney disease (CKD) is a prevalent asymptomatic disease that leads to a significant morbidity and early mortality burden. Current researches have shown that heart issues, identified to as Cardio-Renal Syndrome (CRS) in research, often emerge in patients with renal disease. This disease has the potential to cause sudden cardiac arrest in its later stages. Research on patients with cardio-vascular issues to determine whether their kidneys are affected is valuable, as chronic kidney disease and cardio-vascular disorders are closely linked. Early diagnosis of CKD can enable patients to slow or even reverse disease progression with the help of medicinal interventions. Therefore, in this study we developed an Enhanced Deep Learning (DL)-based technique for the automatic identification of CKD. Digitized electrocardiogram (ECG) data is gathered from Physionet Database's Fantasia (healthy individuals) and PTB (kidney patients). In order to eliminate noise from the ECG measurements, an adaptive median filter is utilized. The significant features are extracted from preprocessed ECG signals. Next, the extracted features are sent into the Enhanced Attention Mechanism with a Long Short-Term Memory (EALSTM) model to classify if a signal is abnormal (CKD) or normal (Non-CKD). To enhance the effectiveness of the EALSTM, its hyper-parameters are optimized using the Adaptive Dingo Optimization (ADO) algorithm. Based to the results of the experiment, the recommended method achieves outstanding 97.65% accuracy, 98.83% precision, 99.21% sensitivity, 98.04% specificity, 99.22% recall, and 99.02% f-measure. The results indicated that the proposed method significantly outperforms other state-of-the-art methods.
Srirenganachiyar et al. (Mon,) conducted a other in Chronic Kidney Disease (CKD). Enhanced Attention Mechanism with Long Short-Term Memory (EALSTM) model vs. Other state-of-the-art methods was evaluated on Classification accuracy for CKD. An Enhanced Attention Mechanism with Long Short-Term Memory (EALSTM) model achieved 97.65% accuracy, 99.21% sensitivity, and 98.04% specificity for automatic CKD classification using ECG signals.