An enhanced machine learning algorithm for type 2 diabetes prognosis with a detailed examination of Key correlates.

ABSTRACT: This study aimed to construct a high-performance prediction and diagnosis model for type 2 diabetic retinopathy (DR) and identify key correlates of DR. This study utilized a cross-sectional dataset of 3,000 patients from the People's Liberation Army General Hospital in 2021. Logistic regression was used as the baseline model to compare the prediction performance of the machine learning model and the related factors. The recursive feature elimination cross-validation (RFECV) algorithm was used to select features. Four machine learning models, support vector machine (SVM), decision tree (DT), random forest (RF), and gradient boost decision tree (GBDT), were developed to predict DR. The models were optimized using grid search to determine hyperparameters, and the model with superior performan

SUBMITTER: Wang X 

PROVIDER: S-EPMC11530678 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature