Indian Health Care System is Ready to Fight Against COVID-19 A Machine Learning Tool for Forecast the Number of Beds

Shakti Nagpal, V. Athavale, A. Saini, Ravindra Sharma
{"title":"Indian Health Care System is Ready to Fight Against COVID-19 A Machine Learning Tool for Forecast the Number of Beds","authors":"Shakti Nagpal, V. Athavale, A. Saini, Ravindra Sharma","doi":"10.1109/PDGC50313.2020.9315825","DOIUrl":null,"url":null,"abstract":"Global research team has announced that the health a management system at world level is in fear from CoV-19. Various statistical analysis has been done to check the preparedness to fight against CoV-19. Recent government responses of the different countries are also taken into the consideration while working for CoV-19 handling. Demographic trends are also added to add further content to potential impact of CoV-19 on healthcare services and system. This pandemic has raised a significant challenge to the economy of the different countries. Availability of beds are calculated on Per thousand people in different countries. Few of the countries analysis like Australia is having 2.6 beds per thousand people, while United Kingdom America is having 2.5 beds preparation over 1000 people. Per capita health spending in UK is marginally below the median. Hospital have been urged by government of different countries to postpone their surgeries and other treatments to provide the proper hospitality to cov-19 patients. India is at 145th place among 195 countries in healthcare access and Quality Index (HAQ)[1]. In this paper we have proposed a machine Learning model to predict the number of beds required as Cov-19 cases are increasing. Our Model Predicts the requirement for beds with 95% accuracy and acceptable p-value.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PDGC50313.2020.9315825","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

Global research team has announced that the health a management system at world level is in fear from CoV-19. Various statistical analysis has been done to check the preparedness to fight against CoV-19. Recent government responses of the different countries are also taken into the consideration while working for CoV-19 handling. Demographic trends are also added to add further content to potential impact of CoV-19 on healthcare services and system. This pandemic has raised a significant challenge to the economy of the different countries. Availability of beds are calculated on Per thousand people in different countries. Few of the countries analysis like Australia is having 2.6 beds per thousand people, while United Kingdom America is having 2.5 beds preparation over 1000 people. Per capita health spending in UK is marginally below the median. Hospital have been urged by government of different countries to postpone their surgeries and other treatments to provide the proper hospitality to cov-19 patients. India is at 145th place among 195 countries in healthcare access and Quality Index (HAQ)[1]. In this paper we have proposed a machine Learning model to predict the number of beds required as Cov-19 cases are increasing. Our Model Predicts the requirement for beds with 95% accuracy and acceptable p-value.
印度卫生保健系统已准备好对抗COVID-19一种预测床位数量的机器学习工具
全球研究小组宣布,世界范围内的卫生管理系统对新冠病毒感到恐惧。开展各项统计分析,检验抗疫准备情况。在处理covid -19的工作中,还考虑了各国政府最近的反应。还增加了人口趋势,以进一步增加新冠肺炎对医疗服务和系统的潜在影响的内容。这一流行病对不同国家的经济提出了重大挑战。不同国家的床位可用性是按每千人计算的。像澳大利亚这样的国家,每千人有2.6张床位,而英国和美国每1000人有2.5张床位。英国的人均医疗支出略低于中位数。各国政府敦促医院推迟手术和其他治疗,以适当款待新冠肺炎患者。在医疗保健可及性和质量指数(HAQ)方面,印度在195个国家中排名第145位。在本文中,我们提出了一个机器学习模型来预测随着covid -19病例的增加所需的床位数量。我们的模型以95%的准确率和可接受的p值预测床位需求。
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信