解决1型戈谢病早期诊断机器学习模型中的特征重要性偏差。

IF 7.3 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Yoshiyasu Takefuji
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引用次数: 0

摘要

Tenenbaum等人利用随机森林、光梯度增强机(Light Gradient Boosting machine, LightGBM)和逻辑回归等算法,提出了一种用于1型戈谢病早期诊断的机器学习模型。虽然这些模型提高了诊断的准确性,但由于其潜在的方法,包括模型特定行为和相关特征,它们也会产生有偏差的特征重要性度量。此外,使用SHAP (SHapley加性解释)继承了这些偏差,使结果的解释复杂化。为了加强模型的可靠性,采用稳健的统计方法如Spearman相关和卡方检验来揭示真实的特征关联是至关重要的。本文强调了在推进1型戈谢病的诊断工具中,需要无偏差的方法,而不是传统的特征重要性指标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Addressing feature importance biases in machine learning models for early diagnosis of type 1 Gaucher disease
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来源期刊
Journal of Clinical Epidemiology
Journal of Clinical Epidemiology 医学-公共卫生、环境卫生与职业卫生
CiteScore
12.00
自引率
6.90%
发文量
320
审稿时长
44 days
期刊介绍: The Journal of Clinical Epidemiology strives to enhance the quality of clinical and patient-oriented healthcare research by advancing and applying innovative methods in conducting, presenting, synthesizing, disseminating, and translating research results into optimal clinical practice. Special emphasis is placed on training new generations of scientists and clinical practice leaders.
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