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Updated: Jun 3, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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评估特征选择方法,以准确诊断糖尿病病

Valeria Maeda-Gutiérrez1, Carlos E Galván-Tejada1, Jorge I Galván-Tejada1

  • 1Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juárez 147, Centro, Zacatecas 98000, Mexico.

Biomedicines
|January 8, 2025
PubMed
概括

这项研究确定了墨西哥患者糖尿病病的关键预测因素. 随机森林的Galgo特征选择方法准确地确定了"肌素"",尿素"和"脂质治疗"作为早期检测的关键风险因素.

关键词:
糖尿病病 糖尿病病特性选择算法 特性选择算法机器学习是机器学习.随机的森林随机的森林有关风险因素的风险因素.

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