JMIR Publications

ABSTRACT

Background:

Complication risks in children and adolescents with Type 1 Diabetes (T1D) can lead to serious health outcomes if not detected early. Despite the availability of clinical data, there remains a gap in interpretable tools that support risk stratification in this age group, particularly in alignment with local clinical guidelines.

Objective:

The purpose of this study is to develop a clinically interpretable model that classifies the risk levels of T1D complications—acute, chronic, and low—using real-world data and expert clinical rules derived from the Saudi Diabetes Clinical Practice Guidelines (SDCPG).

Methods:

A pediatric T1D dataset of 306 cases was preprocessed through structured cleaning and feature engineering. Risk labels were constructed using SDCPG-derived rules. Feature selection was performed using a hybrid approach that combined SHAP analysis with Exhaustive Feature Selection (EFS). A decision tree model was trained and optimized via cross-validation, using the F1 score as the primary performance metric.

Results:

The final model achieved a high mean F1 score of 0.9876 with low variance (0.0189), using only five clinical features: BMI, hypoglycemia, disease duration, HbA1c, and impaired glucose metabolism. These features were consistently ranked as the most influential. The resulting decision tree offered a transparent logic path, enhancing its clinical interpretability and usability.

Conclusions:

This study demonstrates that a simple and interpretable model, guided by national clinical guidelines, can effectively predict the risk levels of T1D complications in children and adolescents. Its strong performance, clarity, and reliance on a small number of clinically meaningful features make it a promising candidate for integration into clinical decision support systems. This supports a shift toward predictive and personalized diabetes care.