This study presents a novel method for heart disease classification by integrating the VGG 19 architecture with a 2D Convolutional Neural Network (CNN). This approach aims to improve diagnostic accuracy and reduce misdiagnosis risks. It involves preprocessing heart images, selecting relevant features, and applying classification algorithms. The method achieved a $\mathbf{9 0 \%}$ accuracy rate in data validation. It uses CNNs for detailed data analysis and comparison, and introduces a hybrid technique combining VGG 19 with a 2D CNN. Various classification models were tested, with the GLCM combined with GoogleNet emerging as the most effective for feature selection and accurate heart disease prediction.
Analyzing and Classification of Heart Disease using VGG19
2024-11-06
793339 byte
Conference paper
Electronic Resource
English