ANALYSIS FOR HEART DISEASE PREDICTION USING DEEP NEURAL NETWORK AND VGG_19 CONVOLUTION NEURAL NETWORK
DOI:
https://doi.org/10.23055/ijietap.2023.30.2.8603Abstract
In the medical field, the prediction of heart disease has been most complicated in recent times. People in the modern day are dying suddenly from heart disease and cardiac attacks. It is very much important to make decisions for heart disease prediction by providing machine learning, deep learning, and data Mining. For heart disease, there is a vast quantity of information accessible on diagnosis, treatment, ECHO, ECG, and other factors. In this study, a unique CNN-based architecture is used to classify the histopathological pictures in the public health care data set for heart disease using the VGG-19. Before classifying the data, a deep neural network is helpful for choosing and extracting its features. The proposed model has two fully connected layers and fifteen convolutional layers, and the training approach comprises free training of the data. Utilizing two separate optimizers, the various activations are compared with their purpose to identify them accurately. The suggested model outperforms excellent performance in comparison to conventional architectures with an accuracy of 95.46%.
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