A 10-year retrospective cohort of diabetic patients in a large medical institution: Utilizing multiple machine learning models for diabetic kidney disease prediction.

ABSTRACT:

Objective

As the prevalence of diabetes steadily increases, the burden of diabetic kidney disease (DKD) is also intensifying. In response, we have utilized a 10-year diabetes cohort from our medical center to train machine learning-based models for predicting DKD and interpreting relevant factors.

Methods

Employing a large dataset from 73,101 hospitalized type 2 diabetes patients at The First Affiliated Hospital of Zhengzhou University, we analyzed demographic and medication data. Machine learning models, including XGBoost, CatBoost, LightGBM, Random Forest, AdaBoost, GBDT (gradient boosting decision tree), and SGD (stochastic gradient descent), were trained on these data, focusing on interpretability by SHAP. SHAP explains the output of the models by assigning an importance

SUBMITTER: Li G 

PROVIDER: S-EPMC11369867 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature