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将可解释的机器学习与用于甲状腺癌复发预测的协同分析策略相结合

Souichi Oka¹, Yoshiyasu Takefuji²

¹Science Park Corporation, 3-24-9 Iriya-Nishi Zama-shi, Kanagawa 252-0029, Japan.

European journal of radiology

|July 15, 2025

概括

在甲状腺癌复发模型中的高预测准确度并不能保证可靠的特征重要性. 结合机器学习和统计方法的综合方法对于稳健的风险因素识别至关重要.

科学领域:

在瘤学瘤学.
生物统计学生物统计学
人工智能的人工智能

背景情况:

可解释的机器学习模型越来越多地用于疾病预测.
XGBoost和SHAP因其预测准确性和可解释性而受欢迎.
对于这些模型中得出的特征重要性可靠性存在担忧.

研究的目的:

批判性地评估Schindele等人的方法. (2025) 关于甲状腺癌复发预测.
突出预测准确性在验证特征重要性排名中的局限性.
为可靠的特征归属提出替代分析框架.

主要方法:

对渐变增强决策树 (GBDT) 模型进行批判性检查,特别是XGBoost.
对SHapley添加式扩展 (SHAP) 对潜在偏差遗传的分析.
倡导整合性分析框架,将机器学习与统计方法相结合.

主要成果:

高预测精度 (95.8%) 和AUROC (0.947) 并不能保证无偏见的特征重要性.
XGBoost模型容易过度拟合,导致偏差的特征重要性估计.
SHAP可以继承和放大底层机器学习模型中存在的偏差.

结论:

像XGBoost这样的可解释模型中的特征重要性排名需要谨慎的解释.
使用诸如高度可变特征选择 (HVFS) 和独立组件分析 (ICA) 等方法的整合性方法是必不可少的.
未来的研究应该优先考虑在癌症复发预测中具有强大和可解释的特征重要性的多方面的策略.

关键词:

功能重要性功能重要性机器学习机器学习莎普利的添加式解释统计验证的统计验证甲状腺癌复发预测预测在XGBoost上使用.

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