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.

翻译：在全球范围内，冠心病是导致死亡的主要原因之一。冠心病的早期检测可改善患者预后并降低死亡率。我们提出了一种结合机器学习与图像处理技术预测冠心病患病风险的新型框架。该框架包含多个阶段：数据分析、基于ReliefF的特征选择、基于3D CNN的分割、通过迁移学习的特征提取、特征融合与分类，以及Adagrad优化。框架第一步通过数据分析识别可能指示冠心病的模式与相关性。随后采用ReliefF特征选择从样本图像中筛选最相关特征。接着利用基于3D CNN的分割技术对视盘与黄斑——冠心病诊断的关键区域——进行分割。通过迁移学习对分割后的感兴趣区域进行特征提取，采用特征融合技术整合提取的特征，并训练分类器预测冠心病患病风险。最后通过Adagrad优化提升分类器性能。我们在包含冠心病患者与非冠心病患者的样本图像数据集上评估该框架。结果表明，与先前使用的支持向量机等分类器相比，该框架在预测冠心病患病风险方面实现了较高的准确率。