Prediction Models for Diabetes in Children and Adolescents: A Review - Livija Cveticanin; Marko Arsenovic (Applied Sciences)
This review aims to present the latest advancements in prediction models for diabetes mellitus, with a particular focus on children and adolescents. It highlights models for predicting both type 1 and type 2 diabetes in this population, emphasizing the inclusion of risk factors that facilitate the identification of potential occurrence and early detection of diabetes in young individuals. Newly identified factors for differentiating between types of diabetes are discussed, alongside an overview of various machine learning and deep learning algorithms specifically adapted for diabetes prediction in children and adolescents. The advantages and limitations of these methods are critically examined. The review underscores the necessity of addressing challenges posed by incomplete datasets and emphasizes the importance of creating a comprehensive data repository. Such developments are essential for enabling artificial intelligence tools to generate models suitable for broad clinical application and advancing early diagnostic and preventive strategies for diabetes in children and adolescents.