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Here, we aim to apply various machine learning algorithms to identify and predict dietary patterns in the Dutch population.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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 (na\u00efve 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 factors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>After comparison, the optimal clustering method, K-means clustering, identified two distinct dietary patterns for both sexes, i.e. Traditional and Health-conscious patterns. The Traditional pattern was characterised by a higher energy intake and consumption of bread, potatoes, red and processed meat, coffee, fats and oils, and sugary drinks. Conversely, a higher intake of fruit, vegetables, tea, nuts, seeds, and breakfast cereals characterised the Health-conscious pattern. The classification models demonstrated moderate predictive accuracies (60\u201368%). According to the classifiers, the most important predictors for both sexes were education level, age, and BMI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Machine learning algorithms can be useful in identifying dietary patterns in population studies. We identified Health-conscious and Traditional patterns in a Dutch population, suggesting tailored public health interventions towards individuals adhering to a Traditional pattern. Future research should improve model validity and reproducibility to enhance its applicability in public health interventions and dietary guidelines.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00394-025-03817-4","type":"journal-article","created":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T04:35:45Z","timestamp":1761194145000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Identifying and predicting dietary patterns in the Dutch population using machine learning"],"prefix":"10.1007","volume":"64","author":[{"given":"Marlijn L.","family":"van Houwelingen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8059-6446","authenticated-orcid":false,"given":"Yinjie","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,23]]},"reference":[{"key":"3817_CR1","unstructured":"WHO, FAO (1998). 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