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

ABSTRACT:

Purpose

Nutritional epidemiological research is shifting its focus from individual nutrients to dietary patterns, which challenges traditional statistical methods. Here, we aim to apply various machine learning algorithms to identify and predict dietary patterns in the Dutch population.

Methods

Data on food consumption, sociodemographic and lifestyle factors from 867 males and 866 females participating in the Dutch National Food Consumption Survey (DNFCS) were analysed. K-means, K-medoids, and hierarchical clustering were compared to identify dietary patterns by sex. Six classifiers (naïve Bayes, K-nearest neighbours, decision tree, random forest, support vector machine and xgboost) were used to predict identified dietary patterns based on sociodemographic and lifestyle fact

SUBMITTER: van Houwelingen ML 

PROVIDER: S-EPMC12549413 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature