The Adaptive Hybrid Activation (ADHA) based Deep Convolutional Neural Network model achieved 98.05% accuracy and 98.60% sensitivity for cardiovascular disease detection on the Challenge 2015 dataset.
Does the ADHA deep learning model improve cardiovascular disease detection and classification compared to established methodologies?
A novel Adaptive Hybrid Activation deep learning model achieved 98.05% accuracy in detecting cardiovascular disease from physiological signals in a public dataset.
Background: Cardiovascular diseases (CVDs) represent a significant global health burden, frequently leading to compromised arterial blood supply to vital organs. Timely and accurate detection is paramount for effective clinical intervention and improved patient outcomes. While traditional deep learning methodologies have shown promise in CVD detection from physiological signals, they often face limitations in predictive accuracy, model interpretability, and generalization across diverse data distributions. Objective: This study introduces a novel Adaptive Hybrid Activation (ADHA) based Deep Convolutional Neural Network model. The primary aim is to advance the state-of-the-art in CVD detection and classification by addressing existing limitations in accuracy, enhancing model generalization, and improving the inherent interpretability of the predictive framework. Methods: The ADHA architecture is meticulously designed with an innovation: an adaptive hybrid activation function module. The adaptive hybrid activation function is engineered to dynamically optimize non-linearity and pattern learning, thereby bolstering the model's classification efficacy. Results: Empirical evaluation, conducted on a single, publicly available dataset (Challenge 2015), demonstrates that the proposed ADHA model achieves notable performance metrics, including 98.05% accuracy, 98.00% F1-score, 98.60% sensitivity, 98.20% specificity, 98.54% negative predictive value (NPV), and 97.56% positive predictive value (PPV). These results indicate superior performance compared to several established state-of-the-art CVD detection methodologies on the evaluated dataset. Conclusion: The ADHA model significantly contributes to the enhancement of CVD detection and classification by addressing challenges related to generalization, feature sparsity, and overfitting inherent in deep learning applications. The presented results underscore the model's compelling performance and suggest its potential for future clinical investigation and application in cardiovascular health monitoring.
Chavan et al. (Tue,) conducted a other in Cardiovascular diseases. Adaptive Hybrid Activation (ADHA) based Deep Convolutional Neural Network model vs. Established state-of-the-art CVD detection methodologies was evaluated on CVD detection and classification accuracy. The Adaptive Hybrid Activation (ADHA) based Deep Convolutional Neural Network model achieved 98.05% accuracy and 98.60% sensitivity for cardiovascular disease detection on the Challenge 2015 dataset.