Prediction Models for Diabetes in Children and Adolescents: A Review
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 i...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/6/2906 |
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| author | Livija Cveticanin Marko Arsenovic |
| author_facet | Livija Cveticanin Marko Arsenovic |
| author_sort | Livija Cveticanin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-303aebfd8177481cb6ab874fb1254a8a |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-303aebfd8177481cb6ab874fb1254a8a2025-08-20T02:42:38ZengMDPI AGApplied Sciences2076-34172025-03-01156290610.3390/app15062906Prediction Models for Diabetes in Children and Adolescents: A ReviewLivija Cveticanin0Marko Arsenovic1Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, SerbiaFaculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, SerbiaThis 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.https://www.mdpi.com/2076-3417/15/6/2906type 1 diabetes (T1D)type 2 diabetes (T2D)machine learning (ML) prediction modeldeep learning (DL) prediction modelartificial intelligence (AI) prediction modelchildren and adolescent diabetes |
| spellingShingle | Livija Cveticanin Marko Arsenovic Prediction Models for Diabetes in Children and Adolescents: A Review Applied Sciences type 1 diabetes (T1D) type 2 diabetes (T2D) machine learning (ML) prediction model deep learning (DL) prediction model artificial intelligence (AI) prediction model children and adolescent diabetes |
| title | Prediction Models for Diabetes in Children and Adolescents: A Review |
| title_full | Prediction Models for Diabetes in Children and Adolescents: A Review |
| title_fullStr | Prediction Models for Diabetes in Children and Adolescents: A Review |
| title_full_unstemmed | Prediction Models for Diabetes in Children and Adolescents: A Review |
| title_short | Prediction Models for Diabetes in Children and Adolescents: A Review |
| title_sort | prediction models for diabetes in children and adolescents a review |
| topic | type 1 diabetes (T1D) type 2 diabetes (T2D) machine learning (ML) prediction model deep learning (DL) prediction model artificial intelligence (AI) prediction model children and adolescent diabetes |
| url | https://www.mdpi.com/2076-3417/15/6/2906 |
| work_keys_str_mv | AT livijacveticanin predictionmodelsfordiabetesinchildrenandadolescentsareview AT markoarsenovic predictionmodelsfordiabetesinchildrenandadolescentsareview |