基于异质集体学习的类失衡性鼻炎的预测
Jingdong Yang1, Biao Jiang1, Zehao Qiu1
1School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China.
Computer methods in biomechanics and biomedical engineering
|April 11, 2024
概括
一个新的分类模型,ARF-OOBEE,通过解决类不平衡,有效地诊断鼻炎. 这种方法可以提高常见和罕见的鼻炎类型的预测准确度,有助于早期临床检测.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 计算生物学 计算生物学
背景情况:
- 临床鼻炎诊断存在挑战,因为不同类型的病例和显著的阶级失衡.
- 现有的分类模型经常在鼻炎预测中扎,因为识别率低,少数群体类的概括性差.
- 这需要先进的计算方法来提高诊断准确性和患者的治疗结果.
研究的目的:
- 引入一种新的综合分类模型,ARF-OOBEE,旨在克服鼻炎预测中的阶级失衡问题.
- 为了提高性能,将多输出分类转化为多标签和多类分类任务.
- 为了提高预测准确度和对多数和少数类鼻炎的概括能力.
主要方法:
- 开发了ARF-OOBEE模型,整合了多标签和多类分类策略.
- 多标签组件根据类不平衡比率动态调整集体学习者.
- 包含一个包装式的Extra-Tree分类器,用于精细分类预测标签和特征重要性分析.
主要成果:
- 在关键指标方面,ARF-OOBEE表现强:灵敏度 (74.9%),特异性 (86.5%),准确性 (92.0%),F1-Score (78.3%),AUC (95.3%) 和G-Mean (79.9%).
- 对461例临床鼻炎病例进行了12次交叉验证,验证了模型的稳定性.
- 针对鼻炎的特征重要性和相关性分析使用决策树纯度进行.
结论:
- 在鼻炎分类方面,ARF-OOBEE模型显著优于现有的方法,特别是在管理阶级不平衡方面.
- 它在改善多数和少数阶级预测方面的有效性支持其在早期临床诊断中的实用性.
- 该研究强调了集成机器学习方法在推进鼻炎诊断和管理方面的潜力.
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