Improved Deep Learning and Feature Fusion Techniques for Chronic Heart Failure
Keywords:
Deep Learning, Machine Learning, Feature Extraction, PCG, Heart sound Classification, HealthcareAbstract
Early detection of heart problems is of paramount importance, given that chronic heart failure remains a leading cause of global mortality. Accurate forecasting of cardiac conditions is crucial for timely intervention and improved patient outcomes. While various machine learning (ML) and deep learning (DL) models have emerged for cardiac disease diagnosis, most struggle to effectively handle high-dimensional healthcare datasets and often fail to significantly enhance chronic heart failure (CHF) diagnosis performance. In this study, we propose a smart healthcare framework that integrates deep learning and feature fusion techniques to predict CHF. Leveraging the PhysioNet datasets, our approach amalgamates features extracted from phonocardiogram (PCG) data. The study introduces novel algorithms, including lightweight CNN, hybrid CNN-autoencoder, and parallel hybrid CNN-autoencoder, offering promising avenues for enhancing CHF detection accuracy and efficiency. The performance of our proposed system is rigorously evaluated against alternative approaches, including feature extraction, machine learning, and traditional deep learning classifiers, using heart sound data. This research aims to advance CHF prediction capabilities, bridging the gap between cutting-edge technology and early cardiac healthcare intervention.
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