An Empirical study and assessment of minority oversampling with Dynamic Ensemble Selection on COVID-19 utilizing Blood Sample

P. Srikanth, C. Behera
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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.
基于动态集合选择的血液样本COVID-19少数过采样实证研究与评价
2019年底在中国爆发的新冠肺炎疫情,给世界造成了巨大灾难性影响。它对全球造成了严重打击,这种破坏大大增加了诊断负担。在大流行地区,临床医生将能够减少工作量,并对新疾病做出正确的诊断,这对机器学习的使用大有帮助。血液测试已成为识别假阳性或假阴性实时rRT-PCR诊断的关键工具。这主要是因为它是一种成本效益高、方便的检测新冠病毒疑似患者的方法。在与COVID-19相关的众多严重后果中,已被确定为COVID-19患者中最普遍的后果之一。这项研究的推动力是covid -19后数据集的稀缺性。在处理地址缺失值的预处理之后,使用SMOTE ENN进行过采样以生成多个实例,并在这些数据集上进行模型训练。然而,已经证明规范动态集成选择优于静态选择和动态选择。结果表明,DI+SMOTEENN+DESKNU超过了现有的基准分类算法,获得了99.6%的最佳准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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