# Prediction Models for Diabetes in Children and Adolescents: A Review
## 6 Future Research  
In spite of the large amount of research on diabetes prediction in children and adolescents, many challenges remain. Let us mention some of them:  
* The natural history of early-onset diabetes remains poorly understood and explained. * Further determining risk factors for diabetes that are specific in children and adolescents and are related to growing up and maturing is necessary. * Some of the risk factors are not sufficiently researched yet. This is the case, for example, with the influence of psychological conditions or disorders in adolescence on the occurrence of diabetes; however, there is an opposite effect between the influence of early-onset diabetes on psychological conditions. The effect of the mutual influence of different risk factors during growing up on the occurrence of diabetes in adolescents. The complexity of early-onset diabetes requires a multidisciplinary approach and collecting a significant amount of data, which represent the risk factors. Modern techniques, along with ML and DL, enable the processing of a large number of data, and the DNN analysis of large quantities that are intertwined with each other. In order to ensure the highest possible prediction accuracy, these AI tools will need to be adapted and adjusted for problem-solving. Thus, future research focused on predicting diabetes in children and adolescents should encompass the following phases:  
Identification of Key Variables and Relationships: Incorporate tools that identify the most relevant variables (family history, BMI, age, average systolic and diastolic blood pressure, fasting glucose, and glycol hemoglobin level), and improve understandings of the relationships between different parameters. This step is crucial to enable better model interpretation and improve prediction accuracy. Development of Interpretable Models: The focus has to be on creating interpretable models that provide clinical insights that are useful for decision-making. Existing models, such as RF, are efficient, but their complexity limits transparency, making it necessary to further refine them for improved usability. Validation Across Diverse Populations: For achieving the generality of the model, there is a need for the validation of large and diverse datasets across multiple populations (various ethnic groups, different genders, and rural and urban population; with various SES, nutrition, and activity levels). This step is vital to enhance model reliability and ensure the generalizability of different patient groups. It can be concluded that these new prediction models would need tools for identification, interpretation and validation based on large dataset. A substantial dataset can be obtained from Electronic Health Records (EHRs). However, these records must be preprocessed to address their inherent incompleteness. On the other hand, for the incomplete dataset with some missing values, another prediction procedure has to be developed in the future. Moving forward, it is essential to find a way to incorporate the missing values into the dataset and the existing models. It is recommended to integrate AI tools and techniques into the preprocessing stage to enhance efficiency and accuracy. To overcome the problem of incompleteness of data or of a small dataset the following recommendations are suggested:  
Synthetic data generation: A model of the DNN type for generating completely new synthetic data which could expand the original dataset has to be developed. To generate more authentic new data from the training dataset, this has to be completed. The model must include the already missing values, which require preprocessing (psychology state or disorder indicators [109], specific features of children, and adolescents, etc.). For the appropriate DL architecture, the generative adversarial network is suggested. Prediction DNN model: A predictive DNN model for diabetes based on the original and synthetic data has to be created.