Knowledge Extraction on Hemodialysis by using Binary and Multiclass with ML models

Dialysis is renal replacement. The kidneys filter blood, but artificial equipment removes water, solutes, and poisons. In persons with acute kidney injury (AKI) or chronic kidney disease (CKD, previously end-stage renal disease, ESRD), dialysis maintains homeostasis (a steady internal environment). It may provide temporary relief from renal function decline, delay a kidney transplant, or last a lifetime for non-transplant patients. In 2010, 2.5 million persons got chronic renal replacement treatment (RRT). Medical professionals are using machine learning to manage hemodialysis patients. In illness data analysis, the random forest classifier is a Machine Learning approach with good accuracy and interpretability. We tried using Machine Learning to change dry weight, the volume status of hemodialysis patients, which needs a complex decision-making process based on many indicators and their physical circumstances. This Machine learning algorithms helps that to take treatment for kindly failure patients. This works explores that the Multi Class (MC) with Ada Boost learning model has the highest accuracy (92.63%). The lowest accuracy is 87.49% for BC with Bagging model. MC with Bagging has 91.51% accuracy and BC with Ada Boost 88.72%. MC with Ada Boost learning model has the highest precision (0.93). Bagging model BC has the lowest precision of 0.86. MC with Ada boost model has highest recall (0.93). Binary Class (BC) with Bagging model has the lowest recall, 0.88. The MC with Bagging has 0.92 recall and the BC with Ada boost 0.89. The MC with Ada Boost model has the highest ROC, 0.94. The lowest ROC is 0.88 for BC using Ada Boost and Bagging models. The MC with Ada Boost model produces the greatest PRC, 0.94. Both Ada Boost and Bagging BCes have 0.88 PRC. This work recommends that the multiclass with Ada Boost performs well on hemodialysis dataset for making treatment of the kidney failure patients.