Full Text Available

Note: Clicking the button above will open the full text document at the original institutional repository in a new window.

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

Saved in:
Bibliographic Details
Published in:Journal of Mother and Child
Format: Online Article RSS Article
Published: 2026
Subjects:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1867664056248172544
collection WordPress RSS
FRELIP Feed Integration
container_title Journal of Mother and Child
description
discipline_display Nurses and Nursing
discipline_facet Nurses and Nursing
format Online Article
RSS Article
genre Journal Article
id rss_article:89617
institution FRELIP
journal_source_facet Journal of Mother and Child
last_indexed 2026-06-11T02:00:41.891Z
publishDate 2026
publishDateSort 2026
record_format rss_article
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
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_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_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_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