Hybrid Artificial Intelligence Model for Detecting Signs of Delayed Child Development - Souza, Daniel Leal; Santos, Isadora Mendes dos; Soares, Caio Johnston; Oliveira Neto, José Pires de; Cassiano, Lucas; Proença Neto, Marco Aurélio; Ramos, Aline Maria Pereira Cruz; Oliveira, Liliane Afonso de; Araújo, Flávia Luciana Guimaraes Marçal Pantoja de; Araújo, Fabrício Almeida; Souza Junior, Gilberto Nerino de; Braga, Marcus de Barros (Anais da Brazilian Conference on Intelligent Systems (BRACIS), 2024)
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.