  Globally, Coronary Heart Disease (CHD) is one of the main causes of death.
Early detection of CHD can improve patient outcomes and reduce mortality rates.
We propose a novel framework for predicting the presence of CHD using a
combination of machine learning and image processing techniques. The framework
comprises various phases, including analyzing the data, feature selection using
ReliefF, 3D CNN-based segmentation, feature extraction by means of transfer
learning, feature fusion as well as classification, and Adagrad optimization.
The first step of the proposed framework involves analyzing the data to
identify patterns and correlations that may be indicative of CHD. Next, ReliefF
feature selection is applied to decide on the most relevant features from the
sample images. The 3D CNN-based segmentation technique is then used to segment
the optic disc and macula, which are important regions for CHD diagnosis.
Feature extraction using transfer learning is performed to extract features
from the segmented regions of interest. The extracted features are then fused
using a feature fusion technique, and a classifier is trained to predict the
presence of CHD. Finally, Adagrad optimization is used to optimize the
performance of the classifier. Our framework is evaluated on a dataset of
sample images collected from patients with and without CHD. The results show
that the anticipated framework accomplishes elevated accuracy in predicting the
presence of CHD. either a particular user with a reasonable degree of accuracy
compared to the previously employed classifiers like SVM, etc.
