Phonocardiogram Signal Analysis Based Premature Cardiac Influence Detection using Resnet50 CNN for Public Health Protection

Phonocardiogram (PCG) signal analysis plays a crucial role in the early detection of cardiac abnormalities, which is essential for public health protection. The premature disease prediction is tedious because the feature analysis takes place dimensionality problems which leads poor accuracy. The prevailing ensemble learning models’ methods doesn’t concentrate disease margin facts and properties during classification produce higher false rate because for adjusting feature margins gets lower accuracy. To resolve this problem, we propose a efficient Fuzzy c-means feature modality based Resnet50-RNN to detect premature cardiac influence detection for improving public health. The research proposes a method for premature cardiac influence detection using a ResNet50 Convolutional Neural Network (CNN) model for early prediction of heart disease. To address the issue of imbalanced data, Synthetic Minority Oversampling Technique (SMOTE) is employed for preprocessing. The Cardiac Disease Influence Rate (CDIR) is then used to identify the maximum deficiency-affected feature margins. Fuzzy c-means is utilized to select the mutual features associated with the heart disease influence rate. The ResNet50 CNN model is employed for classification, enabling the prediction of disease margins by risk category. The proposed system effectively identifies the disease based on the disease-affected scaling margin, leading to early disease impact prediction with higher accuracy, specificity, F1-measure, and lower false rates compared to other existing systems. The implementation also demonstrates reduced time complexity.