Optimized Machine Learning for CHD Detection using 3D CNN-based Segmentation, Transfer Learning and Adagrad Optimization

International Journal of Electrical and Electronics Engineering

© 2023 by SSRG - IJEEE Journal

Volume 10 Issue 3

Year of Publication : 2023

Authors : R. Selvaraj, T. Satheesh, V. Suresh, V. Yathavaraj

10.14445/23488379/IJEEE-V10I3P103

How to Cite?

R. Selvaraj, T. Satheesh, V. Suresh, V. Yathavaraj, "Optimized Machine Learning for CHD Detection using 3D CNN-based Segmentation, Transfer Learning and Adagrad Optimization," SSRG International Journal of Electrical and Electronics Engineering, vol. 10,  no. 3, pp. 20-34, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I3P103

Abstract:

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. For the purpose of predicting as well as categorizing the patient with heart disease, we applied various ML classifier algorithms. By using Random Forest, Multilayer Perception, and Gradient Boosted Tree, the suggested model's intensity was quite exciting. Also, it was capable of forecasting symptoms associated with cardiovascular disease in either a particular user with a reasonable degree of accuracy compared to the previously employed classifiers like SVM, etc.

Keywords:

Coronary heart disease, Machine learning, Feature selection, Optimization, Segmentation, Classifier.

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