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

arXiv: https://arxiv.org/abs/2305.00411 · HTML: https://mlsanity.com/paper/2305.00411/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 46 references that are in the map.
- First posted on arXiv 30 Apr 2023.

## Builds on (most cited first, 5 of 5)

- [Mining Privacy-Preserving Association Rules based on Parallel Processing in Cloud Computing](https://mlsanity.com/paper/2304.10836/) (2023)
- [Outsourced Analysis of Encrypted Graphs in the Cloud with Privacy Protection](https://mlsanity.com/paper/2304.10833/) (2023)
- [IoT-Based Solution for Paraplegic Sufferer to Send Signals to Physician via Internet](https://mlsanity.com/paper/2304.10840/) (2023)
- [Preserving Data Confidentiality in Association Rule Mining Using Data Share Allocator Algorithm](https://mlsanity.com/paper/2304.14605/) (2023)
- [Safe Routing Approach by Identifying and Subsequently Eliminating the Attacks in MANET](https://mlsanity.com/paper/2304.10838/) (2023)

## Abstract (arXiv)

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.