Explainable machine learning model for predicting the risk of significant liver fibrosis in patients with diabetic retinopathy.

ABSTRACT:

Background

Diabetic retinopathy (DR), a prevalent complication in patients with type 2 diabetes, has attracted increasing attention. Recent studies have explored a plausible association between retinopathy and significant liver fibrosis. The aim of this investigation was to develop a sophisticated machine learning (ML) model, leveraging comprehensive clinical datasets, to forecast the likelihood of significant liver fibrosis in patients with retinopathy and to interpret the ML model by applying the SHapley Additive exPlanations (SHAP) method.

Methods

This inquiry was based on data from the National Health and Nutrition Examination Survey 2005-2008 cohort. Utilizing the Fibrosis-4 index (FIB-4), liver fibrosis was stratified across a spectrum of grades (F0-F4). The severity o

SUBMITTER: Zhu G 

PROVIDER: S-EPMC11552118 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature