Artificial intelligence-based diagnosis of diabetic kidney disease using urinary VOC biosensor data.

BACKGROUND: Diabetic kidney disease (DKD) remains a leading cause of chronic kidney disease worldwide. However, current diagnostic methods rely on indirect biomarkers or invasive renal biopsy. This study aimed to evaluate the feasibility of urinary volatile organic compound (VOC) profiling, combined with machine learning, for non-invasive classification of DKD. METHODS: Urine samples were collected from 127 participants divided into four diagnostic groups: healthy controls, patients with type 2 diabetes without nephropathy, biopsy-confirmed DKD, and patients with primary nephrotic syndromes. Samples were analyzed using a chemiresistive VOC biosensor. A total of 15,240 signal-derived features were extracted based on sensor response dynamics. Synthetic Minority Over-sampling Technique (SMOTE) was applied to balance class sizes. Four machine learning classifiers-Random Forest, Support Vector Machine, k-Nearest Neighbors, and Naïve Bayes-were trained and validated using stratified data. Performance was assessed using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). RESULTS: Random Forest achieved the best test performance, with 86% accuracy, 0.91 precision, 0.86 recall, F1-score of 0.86, and an AUC of 0.95. K-fold cross-validation confirmed the model's robustness and generalizability. Random Forest consistently outperformed other models in distinguishing DKD from both diabetic and non-diabetic glomerular diseases, demonstrating its strong discriminative capability. CONCLUSIONS: Urinary VOC-based machine learning models provide proof-of-concept evidence for non-invasive DKD diagnosis. Random Forest, in particular, shows potential as a triage tool to differentiate DKD from other glomerular conditions, which may in the future help reduce reliance on biopsy and support earlier identification in nephrology practice.