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Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method

Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method

Published on: February 19, 2021

Identifying and predicting dietary patterns in the Dutch population using machine learning.

Marlijn L van Houwelingen¹, Yinjie Zhu^2,3

¹Consumption and Healthy Lifestyles Chair Group, Wageningen University & Research, Hollandseweg 1, 6706 KN, Wageningen, The Netherlands.

European Journal of Nutrition

|October 23, 2025

Summary

This summary is machine-generated.

Machine learning identified two dietary patterns in the Dutch population: Traditional and Health-conscious. These findings can inform public health interventions and dietary guidelines.

Keywords:

Classification Cluster analysis Dietary patterns Machine learning

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Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method

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Area of Science:

Nutritional epidemiology
Data science in public health
Dietary pattern analysis

Background:

Shift from single nutrient analysis to dietary patterns presents statistical challenges.
Dietary patterns are crucial for understanding population health outcomes.
Novel statistical methods are needed for dietary pattern identification.

Purpose of the Study:

Apply machine learning algorithms to identify dietary patterns.
Predict dietary patterns using sociodemographic and lifestyle factors.
Analyze dietary patterns in the Dutch population.

Main Methods:

Utilized data from the Dutch National Food Consumption Survey (DNFCS).
Employed K-means, K-medoids, and hierarchical clustering for pattern identification.
Used six classifiers (naïve Bayes, KNN, decision tree, random forest, SVM, xgboost) for prediction.

Main Results:

K-means clustering identified 'Traditional' and 'Health-conscious' dietary patterns.
Traditional pattern: high energy, meat, fats. Health-conscious pattern: high fruit, vegetables, nuts.
Predictive models showed moderate accuracy (60-68%); education, age, and BMI were key predictors.

Conclusions:

Machine learning is effective for identifying dietary patterns in population studies.
Identified patterns offer insights for targeted public health interventions.
Further research needed to enhance model validity for public health applications.