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首页> 外文期刊>The American Journal of Clinical Nutrition: Official Journal of the American Society for Clinical Nutrition >Analysis of meal patterns with the use of supervised data mining techniques--artificial neural networks and decision trees.
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Analysis of meal patterns with the use of supervised data mining techniques--artificial neural networks and decision trees.

机译：使用监督数据挖掘技术-人工神经网络和决策树对进餐模式进行分析。

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BACKGROUND: At present, the analysis of dietary patterns is based on the intake of individual foods. This article demonstrates how a coding system at the meal level might be analyzed by using data mining techniques. OBJECTIVE: The objective was to evaluate the usability of supervised data mining methods to predict an aspect of dietary quality based on dietary intake with a food-based coding system and a novel meal-based coding system. DESIGN: Food consumption databases from the North-South Ireland Food Consumption Survey 1997-1999 were used. This was a randomized cross-sectional study of 7-d recorded food and nutrient intakes of a representative sample of 1379 Irish adults. Meal definitions were recorded by the respondent. A healthy eating index (HEI) score was developed. Artificial neural networks (ANNs) and decision trees were used to predict quintiles of the HEI based on combinations of foods consumed at breakfast and main meals. RESULTS: This study applied both data mining techniques to the food and meal-based coding systems. The ANN had a slightly higher accuracy than did the decision tree in relation to its ability to predict HEI quintiles 1 and 5 based on the food coding system (78.7% compared with 76.9% and 71.9% compared with 70.1%, respectively). However, the decision tree had higher accuracies than did the ANN on the basis of the meal coding system (67.5% compared with 54.6% and 75.1% compared with 72.4%, respectively). CONCLUSIONS: ANNs and decision trees were successfully used to predict an aspect of dietary quality. However, further exploration of the use of ANNs and decision trees in dietary pattern analysis is warranted.
机译：背景：目前，饮食模式的分析是基于单个食物的摄入量。本文演示了如何通过使用数据挖掘技术来分析膳食级别的编码系统。目的：使用基于食物的编码系统和新颖的基于膳食的编码系统，评估监督数据挖掘方法预测基于饮食摄入的饮食质量方面的可用性。设计：使用了《 1997-1999年北南爱尔兰食物消费调查》中的食物消费数据库。这是一项随机横断面研究，对1379名爱尔兰成年人的代表性样本的7天记录的食物和营养摄入量进行了研究。膳食定义由受访者记录。制定了健康饮食指数（HEI）评分。人工神经网络（ANN）和决策树用于根据早餐和主餐所消耗食物的组合来预测HEI的五分位数。结果：本研究将数据挖掘技术应用于基于食物和膳食的编码系统。就基于食物编码系统的HEI五分位数1和5预测能力而言，ANN的准确性比决策树略高（分别为78.7％和76.9％; 71.9％和70.1％）。但是，基于膳食编码系统，决策树的准确性高于ANN（分别为67.5％和54.6％，以及75.1％和72.4％）。结论：人工神经网络和决策树已成功用于预测饮食质量的一个方面。但是，有必要进一步探索在饮食模式分析中使用人工神经网络和决策树。

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