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预测早期儿童的体重指数,使用前1000天的数据
Erika R Cheng1, Ahmet Yahya Cengiz2, Zina Ben Miled3,4
1Division of Children's Health Services Research, Department of Pediatrics, Indiana University School of Medicine, 410 W. 10th Street, Indianapolis, IN, 46220, USA. echeng@iu.edu.
Scientific reports
|May 31, 2023
概括
机器学习模型通过分析最初1000天的风险因素,准确地预测儿童肥胖. 这些模型可以帮助早期的肥胖预防策略.
科学领域:
- 儿科 儿科 儿科
- 公共卫生 公共卫生
- 机器学习 机器学习
背景情况:
- 生命的前1000天对于预防肥胖至关重要.
- 现有的儿童肥胖预测模型往往缺乏产前和婴儿期早期的危险因素.
研究的目的:
- 利用机器学习来识别儿童肥胖的早期生命风险因素.
- 为儿童早期的体重指数 (BMI) 开发准确的预测模型.
主要方法:
- 拉索回归确定了标准测量之外的13个相关特征.
- 使用五倍交叉验证的支持向量回归来构建BMI预测模型.
- 模型对80%的患者数据进行了训练,并对剩余的20%进行了验证.
主要成果:
- 这些模型在30-36,36-42个月和42-48个月的孩子的BMI预测方面取得了很高的准确性.
- 平均平均误差在0.96到1.00之间,标准偏差很小.
- 确定了影响儿童BMI的前1000天的关键风险因素.
结论:
- 机器学习模型可以有效地使用早期生命数据预测儿童BMI.
- 这些预测工具可以支持临床和公共卫生倡议,用于早期预防肥胖.
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