Machine learning-based stratification of prediabetes and type 2 diabetes progression.

BACKGROUND

Diabetes mellitus, a global health concern with severe complications, demands early detection and precise staging for effective management. Machine learning approaches, combined with bioinformatics, offer promising avenues for enhancing diagnostic accuracy and identifying key biomarkers.

METHODS

This study employed a multi-class classification framework to classify patients across four health states: healthy, prediabetes, type 2 Diabetes Mellitus (T2DM) without complications, and T2DM with complications. Three models were developed using molecular markers, biochemical markers, and a combined model of both. Five machine learning classifiers were applied: Random Forest (RF), Extra Tree Classifier, Quadratic Discriminant Analysis, Naïve Bayes, and Light Gradient Boosting Machine. To improve the robustness and precision of the classification, Recursive Feature Elimination with Cross-Validation (RFECV) and a fivefold cross-validation were used. The multi-class classification approach enabled effective discrimination between the four diabetes stages.

RESULTS

The top contributing features identified for the combined model through RFECV included three molecular markers-miR342, NFKB1, and miR636-and two biochemical markers the albumin-to-creatinine ratio and HDLc, indicating their strong association with diabetes progression. The Extra Trees Classifier achieved the highest performance across all models, with an AUC value of 0.9985 (95% CI: [0.994-1.000]). This classifier outperformed other models, demonstrating its robustness and applicability for precise diabetes staging.

CONCLUSION

These findings underscore the value of integrating machine learning with molecular and biochemical markers for the accurate classification of diabetes stages, supporting a potential shift toward more personalized diabetes management.