What question did this study set out to answer?

The research aims to create and validate an interpretable machine learning model for assessing the prevalence probability of gastrointestinal heat retention syndrome in children.

July 4, 2026Journal of Medical Internet ResearchOpen Access

Development and Validation of an Explainable Machine Learning Model to Assess the Prevalence Probability of Gastrointestinal Heat Retention Syndrome in Children: Cross-Sectional Study

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Authors

SHSenlong HouBeijing University of Chinese Medicine JJJiyu JiangBeijing University of Chinese Medicine XLXue LiNanjing Tech University

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Implication

Cross-sectional study develops an explainable model to assess gastrointestinal heat retention syndrome in children, aiding caregivers.

Key Points

The research aims to create and validate an interpretable machine learning model for assessing the prevalence probability of gastrointestinal heat retention syndrome in children.
Conducted a questionnaire survey with 120,198 kindergarten children in Longgang District, Shenzhen, China, excluding samples with missing GHRS information.
Utilized univariate logistic analysis, Least Absolute Shrinkage and Selection Operator regression, and Random Forest for model development and feature selection.
Built an online tool using the optimal machine learning model validated for performance and usability.
Collected 108,447 valid questionnaires, identifying 59 correlates of GHRS including 10 protective and 49 risk factors.
Random Forest model demonstrated high discriminatory performance and was selected as the primary analytical model after validation.
The online tool provides GHRS probability estimates and lifestyle recommendations based on responses to 75 questions.

Cite This Study

Hou et al. (2026) studied this question.

synapsesocial.com/papers/6a48a6b689561a0c2d78ea7d https://doi.org/10.2196/94775

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Development and Validation of an Explainable Machine Learning Model to Assess the Prevalence Probability of Gastrointestinal Heat Retention Syndrome in Children: Cross-Sectional Study

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Implication

Key Points

Cite This Study

Development and Validation of an Explainable Machine Learning Model to Assess the Prevalence Probability of Gastrointestinal Heat Retention Syndrome in Children: Cross-Sectional Study

Discussion

Member takes

Implication

Key Points

Cite This Study

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