Abstract:
The term “Chronic Kidney Disease” (CKD) is used in medicine to describe a number of
disorders that result in kidney damage or a poor Glomerular Filtration Rate (GFR). Medical
advancements in recent years have allowed doctors to apply a wide range of techniques in
the treatment of this illness. Recently, AI and ML have been increasingly adopted as a
useful method for improving healthcare and medical research. The use of Machine
Learning to detect the early symptoms of Kidney Condition is helpful as the disease may
lead to a life-threatening condition. Different machine learning techniques, programs, and
algorithms can be applied together to predict the steady progress of Chronic Kidney
Disease. An appropriate result is produced by a machine-learning algorithm using this
technique, and the algorithm with the highest performance among all others is chosen as
the best one. The system could allow doctors to determine the formation of the disease as
soon as they receive the dialysis report. Also, the report analysis can help to figure out
which elements in the human body are the root cause of Chronic Kidney Disease. Complex
and dynamic algorithms such as Naive Bayes, Random Forest, KNN, Decision Tree,
AdaBoost & XGBoost etc. are needed in order to achieve optimal results in this system.
