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

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

  • By

  • Senlong Hou

  • Jiyu Jiang

  • Xue Li

  • Mingze Yang

  • Qilin Chen

  • Xueyan Ma

  • Xiaohong Gu

  • July 2, 2026

  • 0 min

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  • 1

    Gastrointestinal heat retention syndrome (GHRS) is linked to high-calorie diets and presents with specific gastrointestinal symptoms in pediatric patients.

  • 2

    The diagnostic model for GHRS, based on the Extreme Gradient Boosting algorithm, achieved an internal validation accuracy of 93.03%.

  • 3

    Sociodemographic factors such as age, gender, and household income influence the prevalence of GHRS among children.

  • 4

    Protective factors against GHRS include daily indoor exercise exceeding 30 minutes and higher consumption of vegetables, fish, and soy products.

  • 5

    Current research on GHRS is limited, with no prevalence prediction model developed, restricting its clinical utility.

Original Source(s)

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

  • by Senlong Hou, Jiyu Jiang, Xue Li, Mingze Yang, Qilin Chen, Xueyan Ma, Xiaohong Gu

  • July 2, 2026

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