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Updated: Sep 10, 2025
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Development and internal validation of a machine learning algorithm for the risk of type 2 diabetes mellitus in
Jin-Xia Yang1,2, Yue Liu1,3, Rong Huang4
1School of Medicine, Tongji University, Basic Medical Science, Shanghai, China.
Insights
Machine learning accurately predicts type 2 diabetes mellitus (T2DM) risk in obese children. The Support Vector Machine (SVM) algorithm identified key predictors, enabling early intervention for this growing health concern.
Area of Science:
- Pediatric Endocrinology
- Computational Medicine
- Public Health
Background:
- Childhood obesity is a growing epidemic, increasing the risk of early-onset type 2 diabetes mellitus (T2DM).
- Early identification of high-risk children is critical for implementing preventive strategies against T2DM.
Purpose of the Study:
- To develop and validate a machine learning (ML)-based model for predicting T2DM risk in obese children.
- To compare the efficacy of various ML algorithms in identifying T2DM predictors.
Main Methods:
- A cohort of 292 obese children was followed for at least 1 year.
- Eight ML algorithms were evaluated for T2DM risk prediction.
- The Support Vector Machine (SVM) algorithm was selected for its predictive performance.
Main Results:
- The SVM model achieved an area under the receiver operating characteristic curve of 0.98 and 93.2% accuracy.
- Key predictors identified by SVM include BMI, creatinine, glucose, and HbA1c levels.
- Forty-nine children were diagnosed with T2DM during the study period.
Conclusions:
- An ML-based model, particularly using SVM, can accurately identify obese children at high risk for T2DM.
- This predictive tool has the potential to facilitate early, personalized interventions to prevent T2DM development.
- External validation is recommended to confirm the model's generalizability.
Aim:
We aimed to develop and internally validate a machine learning (ML)-based model for the prediction of the risk of type 2 diabetes mellitus (T2DM) in children with obesity.
Methods:
In total, 292 children with obesity and T2DM were enrolled between July 2023 and February 2024 and followed for at least 1 year. Eight ML algorithms (Decision Tree, Logistic Regression, Support Vector Machine (SVM), Multilayer Perceptron, Adaptive Boosting, Random Forest, Gradient Boosting Decision Tree, and Extreme Gradient Boosting) were compared for their capacity to identify key clinical and laboratory characteristics of T2DM in children and to create a risk prediction model.
Results:
Forty-nine children were diagnosed with T2DM during the follow-up period. The SVM algorithm was the best predictor of T2DM, with the largest area under the receiver operating characteristic curve (0.98) and accuracy (93.2%). The SVM algorithm identified eight predictors: BMI, creatinine, prealbumin, glucose (180 min), glycosylated hemoglobin A1c, thyrotropin, total thyroxine (T4), and free T4 concentrations. Thus, an ML-based prediction model accurately identifies children with obesity at high risk of T2DM. If externally validated, this tool could facilitate early, personalized interventions aimed at preventing T2DM.
Discussion:
The rising prevalence of obesity in childhood is associated with an increase in the risk of early-onset T2DM. Therefore, the early identification of individuals at high risk is crucial to prevent the development of this disease. In a comparative analysis of the performance of multiple ML algorithms, we found that the SVM algorithm was the best predictor of the development of T2DM.
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