Explainable machine learning-based prediction model for diabetic nephropathy

TL;DR

This study develops an explainable machine learning model, specifically using XGBoost, to predict diabetic nephropathy based on serum metabolites, achieving high accuracy and identifying potential biomarkers.

Contribution

The paper introduces a novel XGBoost-based predictive model for diabetic nephropathy that incorporates serum metabolites and explains feature importance with SHAP, outperforming other models.

Findings

XGB model achieved AUC of 0.966 for DN prediction.

Serum metabolites C2, C5DC, Tyr, Ser, Met, C24, C4DC, and Cys are key biomarkers.

Significant interactions between metabolites and diabetes duration were identified.

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 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, calibration curves. We quantify feature importance and interaction effects in the optimal predictive model by Shapley Additive exPlanations (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.