Machine Learning Framework for Early Detection of Chronic Kidney Disease Stages Using Optimized Estimated Glomerular Filtration Rate

• Published in: IEEE access Vol. 13; pp. 78057 - 78072
• Main Authors: Ghosh, Samit Kumar, Widatalla, Namareq, Khandoker, Ahsan H.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Biological system modeling; Biomarkers; Chronic kidney disease; Chronic kidney diseases; CKD-EPI equation; Classification; Creatinine; cystatin C; Decision trees; Diagnosis; Diseases; Effectiveness; Explainable AI; Filtration; glomerular filtration rate; Heuristic methods; Kidney diseases; Machine learning; Mathematical models; Predictive models; Regression models; Robot learning; Root-mean-square errors; serum creatinine; Support vector machines

Chronic Kidney Disease (CKD) is a progressive condition that requires accurate diagnosis and staging for effective clinical management. Conventional CKD diagnosis relies on estimated Glomerular Filtration Rate (eGFR), a measure of kidney function derived from serum biomarkers such as serum creatinin...