# Prediction Models for Diabetes in Children and Adolescents: A Review
## 5 Machine Learning and Deep Learning Models to Predict Diabetes in Children and Adolescents
### Comparison of ML Algorithms  
In ref. [87], various ML algorithms for diabetes prediction are presented. The most often applied ML classifiers are as follows: Naive Bayes (NB), k-Nearest Neighbors (k-NN), Decision Tree (DT), Support Vector Machine (SVM), and Bernoulli Naive Bayes (BNB). The classifiers have different properties. NB is a linear probabilistic classifier based on the simplest Bayesian network model, while BNB is the NB classifier for multivariate Bernoulli models. The k-NN algorithm is a non-parametric, supervised learning classifier, which uses proximity to make classifications or predictions about the grouping of an individual data point. In comparison, NB tends to be faster than k-NN when applied to large amounts of data. DT is an ML algorithm with a hierarchical tree structure. Due to the abundance of ML algorithms that have already been developed, making the appropriate choice is not an easy task. In [88], the comparison of ML algorithms for diabetes prediction is presented. The paper [89] examines the effectiveness of four of them. These are NB, k-NN, SVM, and DT. All these classifiers were evaluated using precision, recall, F1-score, and ROC scores. In the methods, the anthropometric and biochemical data were used. The dataset comprised T2D patients and healthy individuals, incorporating variables such as glycated hemoglobin (HbA1c) [76], BMI [71], fasting blood sugar (FBS), blood pressure [90], and serum lipids (TC, TG, HDL-c, and LDL-c). Among these, NB showed the highest predictive accuracy for diabetic cases, followed by SVM, while k-NN and DT demonstrated lower performance. Recursive feature elimination identified HbA1c, TC, and BMI as the most significant predictors, whereas HDL-c, and LDL-c were deemed less important. In [91], the other five ML models are compared: k-NN, BNB, DT, Logistic Regression (LR), and SVM. In analysis, the features of samples such as glucose levels, BMI, and family history are applied. Among the models, k-NN achieved the highest accuracy and was followed by BNB. Data balancing and dimensionality reduction techniques played a pivotal role in enhancing model performance. While the findings highlight ML's potential in diabetes detection, the study acknowledges its limitations. The dataset's small size and demographic specificity constrain generalizability. This underscores the importance of robust datasets for reliable diabetes prediction and classification. In [92], the aforementioned five standard ML classifiers were applied, and HbA1c and fasting plasma glucose (FPG) were considered as input features. The findings highlight the potential of ML algorithms in facilitating the early detection of T2D and mitigating the disease's impact. [PERSON] [93] examined the two existing prediction models--the Hybrid Prediction Model for T2D and the T2D Mellitus Prediction Model--by introducing a novel ML-basedmethod called the Diabetes Pattern Detection Technique using the Tree Ensemble Clustering Classifier (DDTEC). DDTEC is specifically designed to improve the classification of T1D and T2D. The experimental results highlight the challenges that existing models face in accurately predicting diabetes patterns. In comparison, DDTEC demonstrates superior performance across multiple metrics, including accuracy, recall, specificity, precision, and F-measure. By enhancing early detection and classification, DDTEC establishes itself as a valuable tool for diabetes screening, diagnosis, treatment planning, and holistic patient management. Using supervised ML techniques and key predictors, including physical traits (waist size, leg length, and gender), dietary factors (water, protein, and sodium intake), and demographic variables, valuable insights into diabetes risk in children and adolescents are obtained [94]. Using algorithms such as LR, SVM, RF, XGB tree, and a WVC, the model shows promise in early detection. The limitations include a small sample size and a lack of data distinguishing between diabetes types.