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Child development Assessment is a multifaceted process that incorporates variables of diverse origins in order to identify developmental delays. The present study proposes a hybrid artificial intelligence model, combining first-order logic and fuzzy logic to identify delays in child development. The usage of first-order logic facilitates the integration of large volumes of data, promoting a holistic view. The usage of fuzzy logic enables the treatment of uncertainties and a detailed analysis of variables. The results indicate that the proposed model is effective in mapping delays in child development, as well as in using the data obtained to map the child’s evolution trend.

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Hybrid Artificial Intelligence Model for Detecting Signs of Delayed Child Development

  • 会议论文

  • 发表日期：

    2025年

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  • Daniel Leal Souza,
  • Isadora Mendes dos Santos,
  • Caio Johnston Soares,
  • José Pires de Oliveira Neto,
  • Lucas Cassiano,
  • Marco Aurélio Proença Neto,
  • Aline Maria Pereira Cruz Ramos,
  • Liliane Afonso de Oliveira,
  • Flávia Luciana Guimaraes Marçal Pantoja de Araújo,
  • Fabrício Almeida Araújo,
  • Gilberto Nerino de Souza Junior,
  • Marcus de Barros Braga

摘要

Child development Assessment is a multifaceted process that incorporates variables of diverse origins in order to identify developmental delays. The present study proposes a hybrid artificial intelligence model, combining first-order logic and fuzzy logic to identify delays in child development. The usage of first-order logic facilitates the integration of large volumes of data, promoting a holistic view. The usage of fuzzy logic enables the treatment of uncertainties and a detailed analysis of variables. The results indicate that the proposed model is effective in mapping delays in child development, as well as in using the data obtained to map the child’s evolution trend.