Chronic Kidney Disease Prediction Using Machine Learning Algorithms and the Important Attributes for the Detection

Abstract

In the modern world, chronic kidney disease has become one of the most hazardous diseases. CKD is a condition in which the kidney cannot perform the proper filtering of the blood or it stooped working completely which causes the left toxic into the blood, which leads the patient to death. It is likely impossible to detect CKD in the early stages, and it is very difficult to save patient’s lives in the last stage of CKD. A patient's life can be saved by renal transplant or the early detection the CKD. Machine Learning algorithm techniques have played a very important role in CKD prediction. Past medical test, reports can also be used as a tool for the early detection of renal disease. Machine Learning (ML) Techniques like KNN, Decision Tree, and ANN are used in this review. We have to find out that Decision Tree has shown the best result of 98.60% of accuracy. Generally, the majority of the algorithms are based on supervised learning and classification problem solving. We have explained some important attributes, which play a major role in early CKD Prediction and Detection. Every attribute has its specific effect on CKD. The previous researchers have done many experiments to get the best attribute and best ml technique for the prediction. in this paper, we have studied all related ML techniques, and important Attributes and discussed the measurement factor for CKD prediction.