Interpretable machine learning for predicting chronic kidney disease progression risk
Objective Chronic kidney disease (CKD) poses a major global health burden. Early CKD risk prediction enables timely interventions, but conventional models have limited accuracy. Machine learning (ML) enhances prediction, but interpretability is needed to support clinical usage with both in diagnosti...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2024-01-01
|
| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076231224225 |