NCBI PubMed Central
open access
Comparative Performance of Machine Learning Models Using Food Intake Frequency Versus Vegetable Intake Data to Predict Problematic Mealtime Behaviour in Japanese Preschool Children.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Low-burden metrics for monitoring healthy diets among nonpregnant females aged 15 to 49 years: a multicountry validation analysis using quantitative 24-hour dietary intake data#500013
- Machine learning can improve the development of evidence-based dietary guidelines#265697
- Acceptability and impact on health-related markers of a sustainable dietary pattern: results from a pilot randomized controlled cross-over study.#298922
- Machine Learning-Based Analysis of Emotional Responses to Food Labels: A Case Study of Thai Young Adults#231268
- BCH-GEE Approach to Examine the Association Between Time-Varying Food Environment Classes and Multi-Level Health Outcomes.#193688
Record · ID 281109
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive