Abstract:
Chronic Kidney Disease (CKD) is a term used in the medical profession to represent a
range of illnesses that result in kidney damage or a decreased Glomerular Filtration Rate
(GFR). In recent years, medical advances have made it feasible for doctors to treat this
disease utilizing a variety of different methods, which they have done. Artificial
intelligence and machine learning have gained popularity in recent years as a method of
enhancing medical care and medical research in general, particularly in the field of
medicine. Because Kidney Condition is a potentially fatal disease, it necessitates the
application of machine learning to anticipate when it will develop in the first place. To
forecast the development of "Chronic Kidney Disease," a broad variety of machine learning
techniques, applications, and algorithms may be used in conjunction with one another.
Using this method, a machine-learning algorithm generates a certain output, and the
algorithm that outperformed all of the other algorithms is chosen as the best performance.
This may make it possible for any physicians to identify the beginning of this disease as
soon as the dialysis report is received. It is also possible to identify which component of
the disease is the primary cause of the illness using this technique, which may be
determined from the report study. It is necessary to use more complicated and dynamic
algorithms in order to get the best possible outcome in this system, and these algorithms
include Random Forest, Naïve Bayes, Decision Tree, K-Nearest Neighbor (KNN),
XGBoost, AdaBoost, and others.
