Prediction of COVID-19 by analysis of Breathing Patterns using the Concepts of Machine Learning and Deep Learning Techniques

N. Prathap, Akash Suresh, P. G., T. Manjunath
{"title":"Prediction of COVID-19 by analysis of Breathing Patterns using the Concepts of Machine Learning and Deep Learning Techniques","authors":"N. Prathap, Akash Suresh, P. G., T. Manjunath","doi":"10.1109/CONIT55038.2022.9847821","DOIUrl":null,"url":null,"abstract":"The corona virus, otherwise known as the ‘Covid-19’ is a pandemic that struck the world in December of 2019 and continues on till this day as of writing this research article. It's a virus that targets & affects an individual's immune system. Its most common symptoms include fever, dry cough & tiredness. The most commonly used method used to detect the presence of the COVID-10 virus is the Reverse Transcription Polymerase Chain Reaction Test also known as the RT-PCR test. It is an invasive biomedical procedure that utilizes a nasal swab for the sample collection and provides results in about 24 hours after testing. The research work presented in this paper makes use of parameters such as the breathing patterns, smoking and drinking habits, etc. to detect the likelihood of an individual being proned to the Covid-19 virus. This is achieved by making use of a data set which will be used to train the various machine learning and deep learning algorithms.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 2nd International Conference on Intelligent Technologies (CONIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CONIT55038.2022.9847821","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

The corona virus, otherwise known as the ‘Covid-19’ is a pandemic that struck the world in December of 2019 and continues on till this day as of writing this research article. It's a virus that targets & affects an individual's immune system. Its most common symptoms include fever, dry cough & tiredness. The most commonly used method used to detect the presence of the COVID-10 virus is the Reverse Transcription Polymerase Chain Reaction Test also known as the RT-PCR test. It is an invasive biomedical procedure that utilizes a nasal swab for the sample collection and provides results in about 24 hours after testing. The research work presented in this paper makes use of parameters such as the breathing patterns, smoking and drinking habits, etc. to detect the likelihood of an individual being proned to the Covid-19 virus. This is achieved by making use of a data set which will be used to train the various machine learning and deep learning algorithms.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用机器学习和深度学习技术的概念分析呼吸模式来预测COVID-19
冠状病毒,也被称为“Covid-19”,是一场于2019年12月袭击世界的大流行,一直持续到撰写这篇研究文章的今天。它是一种针对并影响个体免疫系统的病毒。其最常见的症状包括发烧、干咳和疲倦。用于检测COVID-10病毒存在的最常用方法是逆转录聚合酶链反应试验,也称为RT-PCR试验。这是一种侵入性生物医学程序,利用鼻拭子收集样本,并在测试后约24小时内提供结果。本文介绍的研究工作利用呼吸模式、吸烟和饮酒习惯等参数来检测个人感染Covid-19病毒的可能性。这是通过使用一个数据集来实现的,该数据集将用于训练各种机器学习和深度学习算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Analysis of Software Bug Prediction and Tracing Models from a Statistical Perspective Using Machine Learning Design & Simulation of a High Frequency Rectifier Using Operational Amplifier Brain Tumor Detection Application Based On Convolutional Neural Network Classification of Brain Tumor Into Four Categories Using Convolution Neural Network Comparison of Variants of Yen's Algorithm for Finding K-Simple Shortest Paths
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1