Construction of predictive models for type 2 diabetic retinopathy based on the principle of differentiation of syndrome elements

To construct a prediction model for occurrence of type 2 diabetic retinopathy (T2DR) by combining the principle of differentiation of syndrome elements with machine learning (ML).

Cross-sectional data were collected from 273 patients with T2DR and 376 T2DM patients for comparison of routine test results and parameters of syndrome elements in traditional Chinese medicine (TCM) identify the candidate TCM indicators for predicting T2DR occurrence. LASSO regression analysis was used to select the syndrome features with the highest predictive value. Six ML algorithms (LR, ANN, XGBoost, RF, SVM and KNN) were used to construct the prediction models, followed by internal validation of the model and evaluation of the importance of each predictive feature.

LASSO analysis identified the liver, lung, and yang excess syndrome elements, among others, as the most predictive features for T2DR occurrence. Among the 6 ML models, LR and KNN models showed good discrimination power. The LR model yielded highly reliable and robust probability output in global calibration, and the KNN model had good performance for probability predictions. Both the LR and KNN models provided positive net clinical benefits over a wide range of threshold probabilities, but the net benefit curve of the LR model was above that of the KNN model in the whole range, especially in the validation set.

The ML-based prediction models (especially the ML model) combining the features of syndrome elements effectively use the advantage of TCM for predicting disease and allow comprehensive prediction of T2DR in T2DM patients to assist clinical decision-making for early interventions.