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Cited by 86Nutritional Studies and DietArtificial Intelligence in HealthcareData Mining and Machine Learning Applications

Analysis of meal patterns with the use of supervised data mining techniques—artificial neural networks and decision trees

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Hearty, Áine P, Gibney, Michael J·2008·The American Journal of Clinical Nutrition
DOI 10.3945/ajcn.2008.26619
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Abstract

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.

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  • Due to the current limited methods available to collect meal pattern data, most research to date has examined meal patterning, with relatively little focus on meal format and context.

    “Due to the current limited methods available to collect meal pattern data, most research to date has examined meal patterning( 5 , 61 ), with relatively little focus on meal format( 29 ) and context( 32 ).”

    From Understanding meal patterns: definitions, methodology and impact on nutrient intake and diet quality (2015) · cites this paper as [29]

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Authors
Hearty, Á. P., & Gibney, M. J.
Affiliations
University College Dublin (IE)
ORCID
Gibney, Michael J
Venue
The American Journal of Clinical Nutrition
Publisher
Elsevier BV
Published
2008-12
Volume
88
Issue
6
Pages
1632-1642
ISSN
0002-9165 (ISSN-L)
DOI
10.3945/ajcn.2008.26619
OpenAlex
W2005807747
Cited by
86
References
31
Citations by year
2026: 1 · 2025: 3 · 2024: 7 · 2023: 3 · 2022: 8 · 2021: 17 · 2020: 6 · 2019: 5 · 2018: 10 · 2017: 3 · 2016: 5 · 2015: 7 · 2014: 1 · 2013: 4 · 2012: 3
FWCI
0.99
Type
journal article
Language
en
Open access
Licence
Locations
  • American Journal of Clinical Nutrition (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Nutritional Studies and Diet
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
Keywords
meal patterns, dietary habits, diet quality, Healthy Eating Index, artificial neural networks, decision tree, food consumption data, breakfast, main meals
MeSH terms
Adolescent, Adult, Cross-Sectional Studies, Decision Trees, Diet, Diet Surveys, Feeding Behavior, Female, Food Analysis, Humans, Ireland, Male, Middle Aged, Nutrition Surveys, Predictive Value of Tests, Random Allocation, Nutrition Assessment, Databases, Factual, Neural Networks, Computer, Nutrition Policy, Young Adult

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