Predicting Chronic Kidney Disease Using Machine Learning Techniques

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.