REVIEW ARTICLE
A Survey on Deep Learning Models Embed Bio-Inspired Algorithms in Cardiac Disease Classification
Nandakumar Pandiyan1, Subhashini Narayan1, *
Article Information
Identifiers and Pagination:
Year: 2023Volume: 17
E-location ID: e187412072212261
Publisher ID: e187412072212261
DOI: 10.2174/18741207-v16-e221227-2022-HT27-3589-14
Article History:
Received Date: 5/5/2022Revision Received Date: 5/9/2022
Acceptance Date: 17/11/2022
Electronic publication date: 01/02/2023
Collection year: 2023
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Deep learning is a sub-field of machine learning that emerged as a noticeable model in the world, specifically for the disease classification field. This work aims to review the state-of-the-art deep learning models in Cardiac Disease prediction by examining several research papers. In this study, popular datasets listed and analyzed in the prediction process of cardiac disease with their performance using various deep learning techniques are presented. This review emphasizes the latest advancement in the six deep learning models, namely, deep neural networks, convolutional neural networks, recurrent neural networks, extreme learning machines, deep belief networks, and transfer learning with its applications. The important features of cardiac disease used by five different countries have been listed that guide researchers to analyze it for future purposes. Freshly, deep learning models have yielded an extended performance in cardiac disease detection that shows its rapid growth. Specifically, deep learning effectiveness concerted with the bio-inspired algorithms is reviewed. This paper also presents what major applications of deep learning techniques have been grasped in the past decade.
