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Childhood obesity is considered one of the main public health concerns. Research in the field of obesity detection and prevention is moving towards promising solutions thanks to the use of Artificial Intelligence applied to data from cohorts of children. Previous studies have analyzed the data without considering the temporal relationship between them. In this work, sequential pattern mining is used to characterize childhood obesity. Then, original data is represented based on these sequential patterns to feed a case-based reasoning system with the aim to predict childhood obesity. Experiments have been carried out on the data collected from 386 children from Girona and Figueres (Spain).

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Analyzing the Contribution of Sequential Patterns in CBR for Childhood Obesity Prediction

  • 会议论文

  • 发表日期：

    2025年

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  • Beatriz López,
  • Zsofia Prager,
  • Abel López-Bermejo,
  • Judit Bassols

摘要

Childhood obesity is considered one of the main public health concerns. Research in the field of obesity detection and prevention is moving towards promising solutions thanks to the use of Artificial Intelligence applied to data from cohorts of children. Previous studies have analyzed the data without considering the temporal relationship between them. In this work, sequential pattern mining is used to characterize childhood obesity. Then, original data is represented based on these sequential patterns to feed a case-based reasoning system with the aim to predict childhood obesity. Experiments have been carried out on the data collected from 386 children from Girona and Figueres (Spain).