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Updated: Sep 15, 2025

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随机森林和支持载体分类在儿童肥胖和高尿素血的风险预测中的应用和分析
Yuhang Wang1, Shuang Shi1, Xinghua Wei1
1Graduate School, Nantong University, Nantong, Jiangsu, People's Republic of China.
概括
机器学习模型有效地预测了儿童肥胖和高尿血风险. 这些工具有助于早期检测和干预,以改善儿童的长期健康结果.
科学领域:
- 儿科健康 儿科健康
- 代谢障碍 代谢障碍 代谢障碍
- 机器学习在医学中的应用
背景情况:
- 儿童肥胖和高尿素血症正在引起越来越多的公共卫生问题.
- 这些条件通过复杂的代谢相互作用增加心脏代谢疾病的风险.
- 机器学习 (ML) 为儿科风险预测提供了一个有希望的方法.
研究的目的:
- 开发和评估两个ML模型:随机森林 (RF) 和支持矢量分类 (SVC).
- 通过整合临床和生化数据来预测儿童肥胖和高尿血的风险.
- 通过AUC,精度回忆和校准曲线评估模型性能,并通过SHAP分析解释特征重要性.
主要方法:
- 招募了101名儿童 (60名肥胖者,41名肥胖者患有高尿血).
- 数据预处理包括递归特征消除 (RFE),ROSE过量采样和标准化.
- 训练并验证了RF和SVC模型;SHAP分析确定了关键预测因素.
主要成果:
- 射频和SVC模型都实现了高预测性能,AUC为0.96.
- SVC显示出更高的精度和回忆力,适合社区查.
- 射频显示出优异的校准,有利于临床决策.
- 发现的关键预测因素包括GFR,HDL-C和ApoB,以及一些非线性关联.
结论:
- 射频和SVC模型提供了可靠的工具,用于早期预测儿童肥胖和高尿素血的风险.
- 模型是为不同的临床场景量身定制的,支持早期识别和有针对性的干预措施.
- 未来的研究将探索代谢数据和组合方法来提高性能.
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