The MGO-CRNN model improved phonocardiogram classification accuracy to 99.67% on the PASCAL dataset and 99.99% on the PhysioNet 2022 dataset compared to unoptimized models.
Does the MGO-CRNN model improve the classification accuracy of phonocardiogram signals compared to traditional tuning methods?
The MGO-CRNN model demonstrates exceptionally high accuracy in classifying phonocardiogram signals, offering a promising tool for automated cardiovascular disease screening.
Tasa de eventos absoluta: 99.99% vs 99.5%
Abstract Accurate classification of phonocardiogram (PCG) signals is vital for the early detection of cardiovascular diseases (CVDs), a leading cause of global mortality. However, conventional machine learning and deep learning approaches often suffer from suboptimal feature extraction, class imbalance, and inefficient hyperparameter tuning. This study proposes an optimized hybrid CNN-RNN (CRNN) model, enhanced through the Mountain Gazelle Optimization (MGO) algorithm for automated hyperparameter tuning. The model incorporates onset peak detection for precise heartbeat localization, segmentation for structured analysis, and Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. Evaluated on two benchmark datasets—PASCAL and PhysioNet2022—the MGO-CRNN model demonstrated substantial improvements in classification accuracy. Specifically, accuracy on the PASCAL dataset improved from 54.23% to 99.67%, and on PhysioNet 2022 from 99.50% to 99.99% after optimization. The optimized model significantly reduced classification errors and outperformed traditional tuning methods such as grid search and Bayesian optimization. These results underscore the effectiveness of MGO in enhancing model performance and generalization. The integration of advanced signal processing techniques and intelligent optimization provides a robust, real-time solution for AI-assisted cardiac screening and clinical decision support.
Ameen et al. (Sun,) conducted a other in Cardiovascular diseases (abnormal heart sounds) (n=1,568). MGO-CRNN (Mountain Gazelle-Optimized Convolutional Recurrent Neural Network) vs. Unoptimized models and traditional tuning methods was evaluated on Classification accuracy (PhysioNet 2022 dataset). The MGO-CRNN model improved phonocardiogram classification accuracy to 99.67% on the PASCAL dataset and 99.99% on the PhysioNet 2022 dataset compared to unoptimized models.