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.