Explainable Machine Learning-Based Prediction Model for Diabetic Nephropathy

Abstract

The aim of this study is to analyze the effect of serum metabolites on diabetic nephropathy (DN) and predict the prevalence of DN through a machine learning approach. The dataset consists of 548 patients from April 2018 to April 2019 in the Second Affiliated Hospital of Dalian Medical University (SAHDMU). We select the optimal 38 features through a least absolute shrinkage and selection operator (LASSO) regression model and a 10-fold cross-validation. We compare four machine learning algorithms, including extreme gradient boosting (XGB), random forest, decision tree, and logistic regression, by AUC-ROC curves, decision curves, and calibration curves. We quantify feature importance and interaction effects in the optimal predictive model by Shapley additive explanation (SHAP) method. The XGB model has the best performance to screen for DN with the highest AUC value of 0.966. The XGB model also gains more clinical net benefits than others, and the fitting degree is better. In addition, there are significant interactions between serum metabolites and duration of diabetes. We develop a predictive model by XGB algorithm to screen for DN. C2, C5DC, Tyr, Ser, Met, C24, C4DC, and Cys have great contribution in the model and can possibly be biomarkers for DN.

Prevalence of Pre-diabetes, Diabetes and Associated Risk Factors among Staff and Students of Federal Polytechnic, Kaura-Namoda

“…In the 21st century, DM is one of the most common public health challenges, characterized by persistent hyperglycemia and metabolic disturbances caused by an absolute or relative insulin secretion or action deficiency (Pan, et al, 2024). Type 1 DM occurs when the pancreas cannot produce insulin, while type 2 DM occurs when the body cannot effectively use insulin or when there is insufficient insulin production (Yin, et al, 2024). DM-related complications can significantly impact organ function, causing suffering beyond the disease (CDC, 2024).…”