选择,优化和外部验证一个先前存在的机器学习回归算法来估计腰围
1Laboratorio de Nutrición Experimental, Instituto Nacional de Pediatría. Insurgentes Sur 3700, Letra "C", Alcaldía Coyoacán, CDMX, 04530, Mexico.
Computers in biology and medicine
|January 5, 2024
概括
机器学习使用常见数据,如体重和身高,准确地估计腰围 (WC),克服身体质量指数 (BMI) 的局限性,以更好地预测健康风险.
科学领域:
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
- 机器学习 机器学习
背景情况:
- 体重指数 (BMI) 是一种常见的肥胖度量,但在预测健康风险方面存在局限性.
- 腰围 (WC) 是肥胖相关疾病风险的更好的预测指标.
- 现有的数据集通常具有不完整,不准确或缺失的WC数据.
研究的目的:
- 开发和验证用于准确估计腰围 (WC) 的机器学习模型.
- 使用易于使用的预测变量 (体重,身高,年龄,性别) 进行WC估计.
- 提供可靠的方法来评估肥胖,当WC数据是不可用的或不可靠的.
主要方法:
- 系统的数据清理,包括处理缺失值和异常值.
- 对现有的回归算法进行交叉验证,以选择表现最佳的模型.
- 超参数优化和使用各种数据集选择机器学习模型的外部验证.
- 利用公开可用的数据,包括非成年人,以及常见的预测变量.
主要成果:
- 调整的机器学习算法使用有限的预测变量准确地估计了WC.
- 该模型的性能优于以前对WC估计的近似值.
- 这种方法即使在数据集中的WC测量不完整或不可靠的情况下也是有效的.
结论:
- 机器学习为估计WC提供了一个强大的解决方案,改进了传统的BMI指标.
- 这种方法提高了现有数据集对健康风险评估的有用性.
- 该方法可用于估计其他与健康相关的变量.
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