Classifying Type 2 Diabetes Using N-Glycan Profiling and Machine Learning Algorithms

Type 2 diabetes (T2D) continues to present a global public health challenge due to its increasing prevalence. Early diagnosis is critical for preventing complications, but current screening methods often fail to detect early diabetic conditions.

This study aimed to classify T2D patients from healthy individuals using high-resolution N-glycan profiling.

Glycan profiling was performed on serum samples from 161 individuals using capillary electrophoresis with laser-induced fluorescence detection. Different classification methods were fine-tuned using hyperparameter optimization and feature selection techniques, and their performance was comprehensively evaluated based on quality metrics.

The Extra Trees Classifier outperformed the other models with the highest median AUC, demonstrating robust accuracy (0.8982), sensitivity (0.8966), and specificity (0.9000).

N-glycan profiling combined with machine learning provides a promising approach for early T2D detection. The Extra Trees Classifier showed exceptional predictive performance, warranting further investigation with larger datasets to validate its clinical applicability.