Application of machine learning algorithms to identify risk factors for depression in type 2 diabetes mellitus patients: A Taiwan diabetes registry study.

ABSTRACT:

Background

We analyzed variables reported during routine clinical practice using a registrational database to estimate risk factors for depression in people with type 2 diabetes mellitus.

Methods

A Patient Health Questionnaire (PHQ-9) score of 15 was selected as the cut-off for clinically meaningful depression. Missing data was either filled in with a median value, the k -nearest neighbors' method, or the entire variable was removed. Logistic regression, random forest, and decision tree machine learning models were used to decide which factors were most relevant to depression. The accuracy of each algorithm was evaluated with a testing set.

Results

When all variables were included in the logistic regression model, the area under the receiver operating characteristic c

SUBMITTER: Su YW 

PROVIDER: S-EPMC12637159 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature