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预测PCOS患者的TCM模式:探索特征选择方法和多标签机器学习模型

Jiekee Lim¹, Jieyun Li¹, Xiao Feng²

¹School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, PR China.

|August 21, 2024

概括

机器学习 (ML) 和特征选择改善了中国传统医学 (TCM) 对多囊性卵巢综合征 (PCOS) 的诊断. 该RFECV-XGBoost模型准确地分类TCM模式,增强个性化的医疗保健.

科学领域:

生物医学信息学生物医学信息学
计算医学是一种计算医学.
传统中医研究传统中医研究

背景情况:

传统中国医学 (TCM) 通过模式差异化提供个性化的多囊性卵巢综合征 (PCOS) 治疗.
在TCM诊断中的主观性可能导致不一致的结果.
机器学习 (ML) 集成可以为TCM诊断提供客观支持.

研究的目的:

评估特征选择技术和ML算法,用于对PCOS中TCM模式进行分类.
开发一个有效的预测模型,以提高诊断标准化.
支持PCOS患者个性化治疗策略.

主要方法:

利用了432名PCOS患者的数据集,其中有五种不同的TCM模式.
将变量值 (VT) 与先进的特征选择方法进行比较,包括递归特征消除与交叉验证 (RFECV).
评估的ML算法:支持矢量机,物流回归,极端梯度增强 (XGBoost) 和人工神经网络.

主要成果:

差异值将特征从 224 减少到 174.
RFECV证明最有效,确定了67个关键特征.
通过RFECV功能,XGBoost实现了最高的性能:0.7870准确度,0.9519F1得分和0.0481哈明损失.

结论:

该RFECV-XGBoost模型有效地对PCOS患者的TCM模式进行分类.
精确的特征选择对于在TCM诊断中ML模型的性能至关重要.
ML显著推进了TCM模式诊断,促进了精确和个性化的医疗保健.

关键词:

诊断诊断诊断诊断诊断功能选择功能选择机器学习是机器学习.模式分化模式的差异化多囊性卵巢综合征多囊性卵巢综合征预测模型的预测模型.传统的中国医学是传统的中国医学.

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