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一个随机森林算法来评估与慢性病相关的风险因素:观察性研究
Pei Liu1, Yijun Liu2, Hao Liu3
1Department of Mathematics and Physics, Second Military Medical University, Shanghai, China.
Asian/Pacific Island nursing journal
|June 3, 2024
概括
随机森林算法有效地识别慢性病 (CKD) 风险因素,包括年龄和白蛋白尿. 这种机器学习方法有助于早期发现和干预CKD,这对于管理这个日益严重的全球健康问题至关重要.
科学领域:
- 医学研究 医学研究
- 公共卫生 公共卫生
- 机器学习在医疗保健中的应用.
背景情况:
- 慢性病 (CKD) 的患病率和死亡率在全球范围内正在上升.
- 科创板造成的经济负担显著且越来越大.
- 早期检测和干预对于缓解CKD进展和患者成本至关重要.
研究的目的:
- 调查随机森林 (RF) 算法的实用性,以评估CKD风险因素.
- 将RF算法性能与传统的物流回归模型进行比较.
主要方法:
- 在中国上海对40686名个人 (2015-2020年) 查记录的分析.
- 参与者的分类基于膜过率和albuminuria.
- 应用物流回归和射频算法来识别和排名CKD风险因素.
主要成果:
- 后勤回归确定了性别,年龄,肥胖,异常EGFR,退休和保险状况作为重要的CKD风险因素.
- 射频算法强调年龄,白蛋白尿,工作状态和尿液白蛋白-肌素比率作为顶级预测因素.
- 射频模型实现了高预测准确度,AUC为93.15%.
结论:
- 射频算法对CKD风险因素评估具有显著的预测价值.
- 射频可以有效查患有CKD风险的个体.
- 这些发现支持使用RF用于早期CKD干预和预防策略.
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