Development of a predictive model for the risk of microalbuminuria: comparison of 2 machine learning algorithms

作者：Wenyan Long, Xiaohua Wang, Liqin Lu, Zhengang Wei, Jijin Yang · 发表于：Journal of Diabetes & Metabolic Disorders · 年份：2024 · DOI：10.1007/s40200-024-01440-4 · 被引用次数：3 · 研究领域：Chronic Kidney Disease and Diabetes、Retinal Diseases and Treatments、Diabetes Treatment and Management

Purpose: To identify the independent risk variables that contribute to the emergence of microalbuminuria(MAU) in type 2 diabetes mellitus(T2DM), to develop two different prediction models, and to show the order of importance of the factors in the better prediction model combined with a SHAP(Shapley Additive exPlanations) plot. Methods: Retrospective analysis of data from 981 patients with T2DM from March 2021 to March 2023. This dataset included socio-demographic characteristics, disease attributes, and clinical biochemical indicators. After preprocessing and variable screening, the dataset was randomly divided into training and testing sets at a 7:3 ratio. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied to balance the training set. Subsequently, prediction models for MAU were constructed using two algorithms: Random Forest and BP neural network. The performance of these models was evaluated using k-fold cross-validation (k = 5), and metrics such as the area under the ROC curve (AUC), accuracy, precision, recall, specificity, and F1 score were utilized for assessment. Results: The final variables selected through multifactorial logistic regression analysis were age, BMI, stroke, diabetic retinopathy(DR), diabetic peripheral vascular disease (DPVD), 25 hydroxyvitamin D (25(OH)D), LDL cholesterol, neutrophil-to-lymphocyte ratio (NLR), and glycated haemoglobin (HbA1c) were used to construct the risk prediction models of Random Forest...