XGBoost Model for Chronic Kidney Disease Diagnosis

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 17; no. 6; pp. 2131 - 2140
• Main Authors: Ogunleye, Adeola, Wang, Qing-Guo
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Artificial intelligence; chronic kidney disease; clinical decision support system; Computational modeling; Decision support systems; Diagnosis; Diseases; extreme gradient boosting; Feature extraction; Feature selection; Kidney; Kidney diseases; Kidneys; Medical diagnosis; Sensitivity; Sensitivity analysis; World population

Chronic Kidney Disease (CKD) is a menace that is affecting 10 percent of the world population and 15 percent of the South African population. The early and cheap diagnosis of this disease with accuracy and reliability will save 20,000 lives in South Africa per year. Scientists are developing smart s...