Analysis of maternal health risk using computational models

Aditi Ravi, Ruth Sandra J, Sanjana P Joshi, Ashwini Kodipalli, Shoaib Kamal
{"title":"Analysis of maternal health risk using computational models","authors":"Aditi Ravi, Ruth Sandra J, Sanjana P Joshi, Ashwini Kodipalli, Shoaib Kamal","doi":"10.1109/SSTEPS57475.2022.00083","DOIUrl":null,"url":null,"abstract":"Many women during their full term and post pregnancy face various complications which affects the mother’s as well as the fetal health. Diagnosing the variations at an early stage helps in a safer pregnancy period and in decreasing the mortality rate. The rate of maternal fatalities worldwide increased from 151 per 100,000 live births in 2019 to 152 per 100,000 live births in 2020. Due to the advancement in the computational models it is possible for the early prediction of the risks. This research paper is mainly focusing on developing a primal prediction model using classic machine learning algorithms. Among all the algorithms used, Random Forest has outperformed the others with an accuracy of 0.858267.","PeriodicalId":289933,"journal":{"name":"2022 International Conference on Smart and Sustainable Technologies in Energy and Power Sectors (SSTEPS)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Smart and Sustainable Technologies in Energy and Power Sectors (SSTEPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSTEPS57475.2022.00083","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Many women during their full term and post pregnancy face various complications which affects the mother’s as well as the fetal health. Diagnosing the variations at an early stage helps in a safer pregnancy period and in decreasing the mortality rate. The rate of maternal fatalities worldwide increased from 151 per 100,000 live births in 2019 to 152 per 100,000 live births in 2020. Due to the advancement in the computational models it is possible for the early prediction of the risks. This research paper is mainly focusing on developing a primal prediction model using classic machine learning algorithms. Among all the algorithms used, Random Forest has outperformed the others with an accuracy of 0.858267.
利用计算模型分析产妇健康风险
许多妇女在足月和妊娠后期面临各种并发症,影响母亲和胎儿的健康。在早期阶段诊断这些变异有助于更安全的妊娠期和降低死亡率。全球孕产妇死亡率从2019年的每10万活产151例上升到2020年的每10万活产152例。由于计算模型的进步,对风险的早期预测成为可能。本文的研究重点是利用经典的机器学习算法开发一个原始预测模型。在所有使用的算法中,Random Forest以0.858267的准确率优于其他算法。
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 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:604180095
Book学术官方微信