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

Abstract

Abstract 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 and BP neural network, respectively, and the Random Forest model demonstrated superior overall performance (AUC = 0.87, Accuracy = 0.80, Precision = 0.79, Recall = 0.84, Specificity = 0.76, F1 Score = 0.81). The SHAP feature matrix plot revealed that HbA1c, NLR, and 25(OH)D were the three most significant factors in predicting the development of MAU in T2DM, with 25(OH)D acting as an independent protective factor. Conclusion Effective identification of MAU in T2DM, therapeutic strategies for controllable high-risk factors, and prevention or delay of diabetic kidney disease(DKD) can all be achieved with the help of the risk prediction model developed in this study.