Deep learning for early detection of chronic kidney disease stages in diabetes patients: A TabNet approach

Abstract

Chronic kidney disease (CKD) poses a significant risk for diabetes patients, often leading to severe complications. Early and accurate CKD stage detection is crucial for timely intervention. However, it remains challenging due to its asymptomatic progression, the oversight of routine CKD tests during diabetes checkups, and limited access to nephrologists. This study aimed to address these challenges by developing a multiclass CKD stage prediction model for diabetes patients using longitudinal data from the Chronic Renal Insufficiency Cohort (CRIC) study. A novel iterative backward feature selection strategy was employed to determine key predictors of the CKD stage. TabNet, an attention-based deep learning architecture, was used to build classification models in complete and simplified categories. The complete model used 31 features, including complex kidney biomarkers, while the simplified model used 15 features readily available from routine checkups. The performance of TabNet was compared against traditional tree-based ensemble methods (XGBoost, random forest, AdaBoost) and a multi-layer perceptron. Model-specific and model-agnostic explainable AI (XAI) techniques were applied to interpret model decisions, enhancing the transparency and clinical applicability of the proposed approach. The TabNet models demonstrated superior performance, achieving 94.06 % and 92.71 % accuracy in cross-validation for the complete and simplified models, respectively, and 91.00 % and 88.00 % accuracy on test sets. XAI analysis identified serum creatinine, cystatin C, sex, and age as the most influential factors in CKD stage classification. The proposed TabNet models offer a robust approach for early CKD severity detection in diabetes patients, potentially improving clinical decision-making and patient outcomes.