This review highlights how predictive models using machine learning improve CKD diagnosis in India, suggesting new research directions for public health.
Key Points
This review aims to explore the role of machine learning in early prediction and diagnosis of chronic kidney disease (CKD).
Analysis of recent technological advancements in machine learning applications for CKD.
Evaluation of various datasets utilized in predictive models.
Assessment of challenges and limitations of current models.
Identifies the variability in CKD prevalence across India as a public health issue.
Highlights the need for robust and interpretable machine learning models for better predictive accuracy.
Suggests areas for future research to enhance machine learning applications in CKD detection.