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

作者：Gangfeng Zhu, Na Yang, Qiang Yi, Rui‐Hua Xu, Liangjian Zheng, Yunlong Zhu, Junyan Li, Jie Che, Cixiang Chen, Zenghong Lu, Li Huang, Yi Xiang, Tianlei Zheng · 发表于：BMC Medical Informatics and Decision Making · 年份：2024 · DOI：10.1186/s12911-024-02749-z · 被引用次数：10 · 研究领域：Retinal Diseases and Treatments、Retinal Imaging and Analysis、Artificial Intelligence in Healthcare

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 of retinopathy was determined using retinal imaging and segmented into four discrete gradations. A ten-fold cross-validation approach was used to gauge the propensity towards liver fibrosis. Eight ML methodologies were used: Extreme Gradient Boosting, Random Forest, multilayer perceptron, Support Vector Machines, Logistic Regression (LR), Plain Bayes, Decision Tree, and k-nearest neighbors. The efficacy of these models was gauged using metrics, such as the area under the curve (AUC). The SHAP method was deployed to unravel the intricacies of feature importance and explicate the inner workings of the ML model. RESULTS: The analysis included 5,364 participants, of whom 2,116 (39.45%) exhibited notable liver...