A Literature Analysis for the Prediction of Chronic Kidney Diseases

Abstract

It is a global health issue that will, nowadays, affects millions of people with severe relief on their quality of life due to the presence of Chronic Kidney Disease (CKD). Many can progress from short-term to long term kidney failure which has complications such as anaemia, osteoporosis, cardiovascular diseases and end stage renal disease. This makes early identification and management of CKD paramount especially for the slow progression of the disease and better results for the patients. The current research aims at focusing on the capability of trained machine learning models to identify community, clinical, and laboratory variables to estimate the likelihood of the initial identification of CKD. Demographic factors which include age, sex, race and economic status are fundamental in determining possibility of contracting CKD. Proteinuria, serum creatinine and estimated glomerular filtration rate (eGFR) are the essential markers of the renal function and CKD development. Algorithms from the machine learning family such as decision trees, random forests as well as the neural networks are used to find out the patterns that may be associated with the asymptomatic high risk patients and in turn help in the prediction of risk towards development of CKD. This holistic approach improves identification and management of risk factors; perhaps keeping off or remitting the early development of CKD.