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在诊断阴道脱落时缺少数据归算,预测和特征选择

Mingxuan Fan¹, Xiaoling Peng¹, Xiaoyu Niu^2,3

¹Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College, Zhuhai, 519087, China.

BMC medical research methodology

|November 6, 2023

概括

生成对抗性归算网 (GAIN) 有效地归算临床数据,提高了阴道脱落预测的准确性. 断裂的适应性脊 (BAR) 有效地选择关键的风险因素,提高诊断的解释性和准确性.

科学领域:

医疗信息学医学信息学
临床数据分析临床数据分析
机器学习在医疗保健中的应用

背景情况:

临床数据收集容易缺失值,可能会损害诊断准确度.
丢弃不完整的样本可能导致偏见或不准确的医学诊断.
有效的数据归算方法对于最大限度地利用有价值的临床数据集至关重要.

研究的目的:

评估和比较不完整的临床数据的流行的数据归算技术.
评估归算对阴道脱落预测准确性的影响.
用可变选择方法对归算数据来确定阴道脱落的重大风险因素.

主要方法:

五种归算方法 (平均值,EM,KNN,DAE,GAIN) 应用于一个大型的临床数据集 (28,274例).
通过使用七个分类器的分类标准和诊断准确度来评估性能.
三种变量选择方法 (LASSO,SCAD,BAR) 用于在归算数据上进行特征选择.

主要成果:

生成对抗性归因网 (GAIN) 在归因临床数据集方面表现出卓越的表现.
归算变量选择预测框架提高了阴道脱落的诊断准确度.
破碎的自适应脊 (BAR) 从67个中确定了9个显著特征,预测损失最小,优于LASSO和SCAD.

结论:

拟议的框架有效地处理缺少的临床数据,提高诊断准确度.
对于这种类型的数据,GAIN是一种非常有效的归算方法.
BAR是一种优质的变量选择技术,用于识别假定临床数据集中的关键风险因素.

关键词:

分类分类分类分类.诊断阴道脱落的诊断功能选择功能选择产生性的对抗性归算.缺失的数据归算缺失的数据归算

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