Analysis of convolutional recurrent neural network classifier for COVID-19 symptoms over computerised tomography images

Srihari, K.; Yuvaraj, N.; Idrees, Barzan Abdulazeez; Arulprakash, P.; Ranganathan, Vijayakumar; Udayakumar, E.; Dhinakar, P.

doi:10.1504/ijcat.2021.120453

Cited by 14 publications

(1 citation statement)

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“…This comes as the prevalence of data from the internet of things (IoT) grows within healthcare systems. In order to investigate previously unknown risk factors, current efforts to standardise medical data, and efforts to organise national health screening data ( 26–28 ), we will first standardise medical data. These risk variables may have a correlation with the occurrence of the disease, which means that they could offer insights into the mechanisms underlying the disease.…”

Section: Introductionmentioning

confidence: 99%

Cardiovascular diseases prediction by machine learning incorporation with deep learning

Subramani¹,

Varshney

Anand

et al. 2023

Front. Med.

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It is yet unknown what causes cardiovascular disease (CVD), but we do know that it is associated with a high risk of death, as well as severe morbidity and disability. There is an urgent need for AI-based technologies that are able to promptly and reliably predict the future outcomes of individuals who have cardiovascular disease. The Internet of Things (IoT) is serving as a driving force behind the development of CVD prediction. In order to analyse and make predictions based on the data that IoT devices receive, machine learning (ML) is used. Traditional machine learning algorithms are unable to take differences in the data into account and have a low level of accuracy in their model predictions. This research presents a collection of machine learning models that can be used to address this problem. These models take into account the data observation mechanisms and training procedures of a number of different algorithms. In order to verify the efficacy of our strategy, we combined the Heart Dataset with other classification models. The proposed method provides nearly 96 percent of accuracy result than other existing methods and the complete analysis over several metrics has been analysed and provided. Research in the field of deep learning will benefit from additional data from a large number of medical institutions, which may be used for the development of artificial neural network structures.

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Section: Introductionmentioning

confidence: 99%