从生活方式问题中预测习惯性饮水量
H Tanaka1, K Mizuma, Y Nakamura
1Division of Information Science, Nara Institute of Science and Technology, Ikoma, Nara, Japan. hiroki-tan@is.naist.jp.
European review for medical and pharmacological sciences
|October 2, 2023
概括
一个新的简化问卷 (SQW) 使用机器学习准确预测习惯性饮水量. 与传统的饮食记录相比,这种方法提供了一种更快,更简单的方法来评估水分.
科学领域:
- 营养科学 营养科学
- 生物统计学 生物统计学
- 计算生物学 计算生物学
背景情况:
- 评估水摄入量的传统方法,如选择性召回和描述性饮食记录,是劳动密集的.
- 准确和有效的方法来确定习惯性饮用水的摄入量对于健康管理至关重要.
研究的目的:
- 开发和验证一个简化的生活方式问卷来预测习惯性饮水量 (SQW).
- 评估SQW的准确性和效率,与已建立的饮食评估方法相比.
主要方法:
- 利用众包和机器学习 (随机森林) 来从305个生活方式问题中开发SQW.
- 员工合成少数群体过量抽样和回归分析用于模型培训.
- 在一个横截面研究中,与选择性回忆和描述性饮食记录对抗SQW的验证,数据按性别分层.
主要成果:
- 在SQW中,与选择性回忆方法相比,总摄入水的Spearman相关系数为0.60.
- 对于总摄入的水,观察到0.50的皮尔森相关系数.
- 验证证实了各种来源 (非酒精饮料,酒精,食物) 和人口因素 (性别,季节) 的相关性.
结论:
- 简化问卷 (SQW) 提供了一种快速而准确的方法来评估习惯性饮水量.
- 预计SQW的传播将通过更容易确定个人用水量来加强健康管理.
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