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机器学习方法用于预测美国流行零食中的脂肪酸类,使用NHANES数据

Christabel Y E Tachie¹, Daniel Obiri-Ananey², Nii Adjetey Tawiah³

¹Food Science and Biotechnology Program, Department of Human Ecology, College Agriculture, Science and Technology, Delaware State University, 1200 N DuPont Highway, Dover, DE 19901, USA.

|August 12, 2023

概括

机器学习模型有效地分析了美国零食的脂肪含量. 随机森林模型在从国家调查数据中预测和,单不和和多不和脂肪酸方面表现最好.

科学领域:

营养科学营养科学
数据科学数据科学数据科学
计算健康计算健康

背景情况:

美国成年人经常食用高卡路里,高糖,高盐和高脂的零食.
过多的和脂肪酸 (SFA) 摄入与心血管疾病风险有关.
国家健康和营养检查调查 (NHANES) 数据提供了对美国饮食习惯的见解.

研究的目的:

评估各种机器学习 (ML) 回归模型在预测美国零食中SFA,单不和脂肪酸 (MUFA) 和多不和脂肪酸 (PUFA) 含量的有效性.
用ML评估不同类型脂肪酸的总脂肪含量的变化.

主要方法:

使用了美国零食消费的NHANES 2017-2018数据.
应用ML回归模型:人工神经网络 (ANN),决策树 (DT),k-最近邻居 (KNN),支向量机器 (SVM) 和整体方法 (例如,随机森林,极端梯度提升).
使用平均平方误差 (MSE) 和R平方 (R2) 评分评估模型性能.

主要成果:

K-近邻和决策树显示SFA,MUFA和PUFA的预测准确度中等.
人工神经网络表现出令人满意的性能.
合并方法,特别是随机森林,优于线性回归和其他模型,实现了预测所有脂肪酸类别的最低MSE.

结论:

机器学习,特别是像Random Forest这样的组合方法,是分析像NHANES这样复杂的饮食数据的强大工具.
准确预测零食中的脂肪酸概况可以为与心血管疾病预防有关的公共卫生策略提供信息.

关键词:

尼汉斯 (NHANES) 是一个名人.能量密集的食物能量密集的食物脂肪酸类别的脂肪酸类别.机器学习是机器学习.热门的小吃热门的小吃

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