Machine learning-based identification and related features of depression in patients with diabetes mellitus based on the Korea National Health and Nutrition Examination Survey: A cross-sectional study.

ABSTRACT: Patients with diabetes mellitus (DM) are twice as likely as nondiabetic individuals to develop depression, which is a prevalent but often undiagnosed psychiatric comorbidity. Patients with DM who are depressed have poor glycemic control, worse quality of life, increased risk of diabetic complications, and higher mortality rate. The present study aimed to develop machine learning (ML) models that identify depression in patients with DM, determine the best performing model by evaluating multiple ML algorithms, and investigate features related to depression. We developed six ML models, including random forest, K-nearest neighbor, support vector machine (SVM), Adaptive Boosting, light gradient-boosting machine, and Extreme Gradient Boosting, based on the Korea National Health and Nutrition Exa

SUBMITTER: Lee JY 

PROVIDER: S-EPMC10343154 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature