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通过机器学习,对儿童和青少年的短期和长期体重状况的关键预测因素进行稳健的识别
Hengyan Liu1, Yang Leng2, Yik-Chung Wu2
1School of Nursing, The University of Hong Kong, Pokfulam, Hong Kong SAR, China.
Frontiers in public health
|October 9, 2024
概括
机器学习有效地利用重量,身高和自尊等关键预测因素预测儿童体重问题. 早期识别这些因素可以为青少年健康提供有针对性的干预措施.
科学领域:
- 儿科健康 儿科健康
- 机器学习应用 机器学习应用
- 预测分析 (Predictive Analytics) 是一种分析方法.
背景情况:
- 早期发现有体重问题风险的儿童对于及时干预至关重要.
- 机器学习 (ML) 为分析复杂的高维数据提供了强大的工具.
- 有效的特征选择对于确定有针对性的干预措施的关键预测因素至关重要.
研究的目的:
- 确定一套强大而最小的预测因子,用于早期检测儿童和青少年的短期和长期体重问题.
- 利用特征选择技术来提高预测模型的准确性和可解释性.
- 通过确定与体重状况相关的最有影响力的因素,为目标干预提供信息.
主要方法:
- 利用人口统计,身体和心理健康数据,在1,3和5年的时间内模拟体重状况.
- 采用了四种特征选择方法 (Chi-Square,信息获取,随机森林,XGBoost) 与六种ML方法.
- 使用Jaccard索引,Spearman和Pearson的相关性评估特征选择稳定性;使用准确度指标评估模型.
主要成果:
- 使用XGBoost的Chi-Square测试分别确定了1年,3年和5年的预测的6,9和13个关键特征.
- 在预测窗口中实现了高预测准确度 (0.82,0.73,0.70) 和AUC (宏:0.94-0.83,微:0.96-0.92).
- 一贯确定体重,身高,性别,自尊评分和年龄作为关键预测因素;心理/社会因素对长期预测很重要.
结论:
- ML有效地识别了儿童和青少年体重状况的关键预测因素.
- 除了人体测量之外,心理和社会福祉因素是关键的预测因素.
- 这些发现可以指导针对年轻人体重管理的有针对性的干预措施的制定.
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