Prediction of Incident Diabetic Retinopathy in Adults With Type 1 Diabetes Using Machine Learning Approach: An Exploratory Study.

ABSTRACT:

Background

Early detection and intervention are crucial for preventing vision-threatening diabetic retinopathy (DR) in adults with type 1 diabetes (T1D). This exploratory study uses machine learning on continuous glucose monitoring (CGM) data to identify factors influencing DR and predict high-risk individuals for timely intervention.

Methods

Between June 2018 and March 2022, adults with T1D with incident DR or no retinopathy (control) were identified. The CGM data were collected retrospectively for up to seven years before the date of defining incident DR or no retinopathy. A mixture of three machine learning algorithms was trained and evaluated in two different scenarios, using different glycemic features extracted from CGM traces (scenario 1), and the two principal compon

SUBMITTER: Montaser E 

PROVIDER: S-EPMC11571610 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature