Complication Risk Classification in Children and Adolescents With Type 1 Diabetes: Interpretable Machine Learning Study Based on Saudi Clinical Guidelines

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: F1-score of 0.9876

• Primary Outcome: Risk classification accuracy using F1-score metric

• Nnt Or Sensitivity: Model uses 5 clinical features: BMI, hypoglycemia, disease duration, HbA1c, impaired glucose metabolism

• Confidence Interval: Not reported (variance 0.0189)

Clinical Application

High feasibility using routine clinical parameters available in standard diabetes care Limited direct applicability due to development using Saudi guidelines; would require validation against Australian Diabetes Society guidelines and local population characteristics Children and adolescents with type 1 diabetes in similar healthcare settings

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. METHODS: A pediatric T1D dataset comprising of 306 patients was preprocessed through structured cleaning and feature engineering. Risk labels were constructed using Saudi Diabetes Clinical Practice Guidelines-derived rules. Feature selection was performed using a hybrid approach that combined the SHAP (Shapley Additive Explanations) analysis with exhaustive feature selection. 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 a low variance of 0.0189, using only 5 clinical features: BMI, hypoglycemia, disease duration, hemoglobin A1c, 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.