Machine learning regression algorithms to predict short-term efficacy after anti-VEGF treatment in diabetic macular edema based on real-world data.

ABSTRACT: The objective of this retrospective study was to predict short-term efficacy of anti-vascular endothelial growth factor (VEGF) treatment in diabetic macular edema (DME) using machine learning regression models. Real-world data from 279 DME patients who received anti-VEGF treatment at Ineye Hospital of Chengdu University of TCM between April 2017 and November 2022 were analyzed. Eight machine learning regression models were established to predict four clinical efficacy indicators. The accuracy of the models was evaluated using mean absolute error (MAE), mean square error (MSE) and coefficient of determination score (R²). Multilayer perceptron had the highest R² and lowest MAE among all models. Regression tree and lasso regression had similar R², with lasso h

SUBMITTER: Shi R 

PROVIDER: S-EPMC10618454 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature