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基于多个机器学习算法,建立了小学年龄儿童前向头部姿势障碍的预测模型.

Hongjun Tao1, Yang Wen2, Rongfang Yu3

  • 1Department of Physical and Education, Anhui Jianzhu University, Hefei, China.

Frontiers in bioengineering and biotechnology
|June 16, 2025
PubMed
概括

儿童向前的头部姿势可以使用机器学习来预测. 关键指标包括年龄,体重指数 (BMI) 和体重,使早期干预成为可能.

关键词:
前进的头部姿势 前进的头部姿势机器学习是机器学习.小学年龄的孩子们.风险预测模型的风险预测模型莎普利的添加式解释算法

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科学领域:

  • 儿科 儿科 儿科
  • 生物统计学 生物统计学
  • 计算机科学 计算机科学

背景情况:

  • 前进的头部姿势在小学儿童中很普遍,通常与久坐行为和学术压力有关.
  • 目前的查方法在预测这种情况时缺乏准确性和及时性.

研究的目的:

  • 确定小学儿童前进头部姿势的敏感预测指标.
  • 用 LASSO 回归和 SHAP 分析开发和比较用于风险预测的机器学习模型.

主要方法:

  • 一项横截面研究包括514名小学儿童.
  • 拉索回归确定了风险因素;建立了六个机器学习模型 (KNN,LGBM,XGBoost,RF,LM,SVM).
  • 随机森林模型因其卓越的性能而被选中,并使用SHAP进行解释.

主要成果:

  • 年龄,体重,BMI,性别和作业时间被 LASSO 确定为显著的风险指标.
  • 随机森林模型实现了最高的预测准确性 (AUC = 0.865).
  • SHAP分析强调BMI,体重和年龄是最有影响力的预测因素,BMI是主要因素.

结论:

  • 一个基于随机森林的模型证明了中国小学儿童前进头部姿势的高预测准确度.
  • 监测BMI,体重和年龄对于早期发现和预防策略至关重要.