{"title":"An Empirical study and assessment of minority oversampling with Dynamic Ensemble Selection on COVID-19 utilizing Blood Sample","authors":"P. Srikanth, C. Behera","doi":"10.1109/ICEMIS56295.2022.9914231","DOIUrl":null,"url":null,"abstract":"The COVID-19 virus disease outbreak that erupted in China at the end of 2019 has had a tremendous and disastrous impact on the rest of the world. It has struck the globe to its core, and the destruction has substantially increased the diagnostic burden. In the pandemic zone, clinicians will be able to cut down on their workload and get the right diagnosis of the new disease great to the use of machine learning. A blood test has emerged as a critical tool for identifying false-positive or false-negative real-time rRT-PCR diagnostics. Notably, this is mostly because it is such a cost-effective and convenient method of detecting probable COVID-19 patients. Among the numerous hard consequences associated with COVID-19 illness has been established as one of the most prevalent among COVID-19 patients. The impetus for this research is the scarcity of post-COVID-19 dataset. Following pre-processing to manage address missing values, oversampling with SMOTE ENN is used to generate several instances and model training is carried out on these data sets. However, it has been demonstrated that normatively dynamic ensemble selection outperforms static selection and dynamic selection. The DI+SMOTEENN+DESKNU exceed existing benchmark Classification algorithms and obtain the best accuracy of 99.6%, according the results.","PeriodicalId":191284,"journal":{"name":"2022 International Conference on Engineering & MIS (ICEMIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Engineering & MIS (ICEMIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEMIS56295.2022.9914231","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 COVID-19 virus disease outbreak that erupted in China at the end of 2019 has had a tremendous and disastrous impact on the rest of the world. It has struck the globe to its core, and the destruction has substantially increased the diagnostic burden. In the pandemic zone, clinicians will be able to cut down on their workload and get the right diagnosis of the new disease great to the use of machine learning. A blood test has emerged as a critical tool for identifying false-positive or false-negative real-time rRT-PCR diagnostics. Notably, this is mostly because it is such a cost-effective and convenient method of detecting probable COVID-19 patients. Among the numerous hard consequences associated with COVID-19 illness has been established as one of the most prevalent among COVID-19 patients. The impetus for this research is the scarcity of post-COVID-19 dataset. Following pre-processing to manage address missing values, oversampling with SMOTE ENN is used to generate several instances and model training is carried out on these data sets. However, it has been demonstrated that normatively dynamic ensemble selection outperforms static selection and dynamic selection. The DI+SMOTEENN+DESKNU exceed existing benchmark Classification algorithms and obtain the best accuracy of 99.6%, according the results.