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Comparative Performance of Machine Learning Models Using Food Intake Frequency Versus Vegetable Intake Data to Predict Problematic Mealtime Behaviour in Japanese Preschool Children

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Published in:Journal of Mother and Child
Format: Online Article RSS Article
Published: 2026
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container_title Journal of Mother and Child
description
discipline_display Nurses and Nursing
discipline_facet Nurses and Nursing
format Online Article
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genre Journal Article
id rss_article:89617
institution FRELIP
journal_source_facet Journal of Mother and Child
last_indexed 2026-06-20T21:43:32.555Z
publishDate 2026
publishDateSort 2026
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spellingShingle Comparative Performance of Machine Learning Models Using Food Intake Frequency Versus Vegetable Intake Data to Predict Problematic Mealtime Behaviour in Japanese Preschool Children
Nurses and Nursing
General
Nurses and Nursing
sub_discipline_display General
sub_discipline_facet General
subject_display Nurses and Nursing
General
Nurses and Nursing
subject_facet Nurses and Nursing
General
Nurses and Nursing
title Comparative Performance of Machine Learning Models Using Food Intake Frequency Versus Vegetable Intake Data to Predict Problematic Mealtime Behaviour in Japanese Preschool Children
title_alt Rendimiento comparativo de modelos de aprendizaje automático utilizando datos de frecuencia de ingesta de alimentos versus datos de ingesta de vegetales para predecir comportamiento problemático a la hora de comer en niños preescolares japoneses
Performance comparative de modèles d'apprentissage automatique utilisant la fréquence de consommation alimentaire versus les données de consommation de légumes pour prédire les comportements alimentaires problématiques chez les enfants d'âge préscolaire japonais
Desempenho Comparativo de Modelos de Aprendizado de Máquina Usando Dados de Frequência de Ingestão Alimentar versus Dados de Consumo de Vegetais para Predizer Comportamento Alimentar Problemático em Crianças Pré-Escolares Japonesas
title_auth Comparative Performance of Machine Learning Models Using Food Intake Frequency Versus Vegetable Intake Data to Predict Problematic Mealtime Behaviour in Japanese Preschool Children
title_es_txt Rendimiento comparativo de modelos de aprendizaje automático utilizando datos de frecuencia de ingesta de alimentos versus datos de ingesta de vegetales para predecir comportamiento problemático a la hora de comer en niños preescolares japoneses
title_fr_txt Performance comparative de modèles d'apprentissage automatique utilisant la fréquence de consommation alimentaire versus les données de consommation de légumes pour prédire les comportements alimentaires problématiques chez les enfants d'âge préscolaire japonais
title_full Comparative Performance of Machine Learning Models Using Food Intake Frequency Versus Vegetable Intake Data to Predict Problematic Mealtime Behaviour in Japanese Preschool Children
title_fullStr Comparative Performance of Machine Learning Models Using Food Intake Frequency Versus Vegetable Intake Data to Predict Problematic Mealtime Behaviour in Japanese Preschool Children
title_full_unstemmed Comparative Performance of Machine Learning Models Using Food Intake Frequency Versus Vegetable Intake Data to Predict Problematic Mealtime Behaviour in Japanese Preschool Children
title_pt_txt Desempenho Comparativo de Modelos de Aprendizado de Máquina Usando Dados de Frequência de Ingestão Alimentar versus Dados de Consumo de Vegetais para Predizer Comportamento Alimentar Problemático em Crianças Pré-Escolares Japonesas
title_short Comparative Performance of Machine Learning Models Using Food Intake Frequency Versus Vegetable Intake Data to Predict Problematic Mealtime Behaviour in Japanese Preschool Children
title_sort comparative performance of machine learning models using food intake frequency versus vegetable intake data to predict problematic mealtime behaviour in japanese preschool children
topic Nurses and Nursing
General
Nurses and Nursing
url https://sciendo.com/article/10.34763/jmotherandchild.20263001.d-25-00036