A Comparative Evaluation for Kidney Failure Prediction with Machine Learning Technologies

The study's main goal is to identify if a person has chronic renal illness or not. The medical condition known as chronic kidney failure damages overall kidneys' ability to collect harmful substances from your blood and keep good health in general. Some causes for chronic kidney disease contain lower blood pressure, anemia, weaker bones, poor diet, and trauma. Some causes for chronic kidney disease contain lower blood pressure, anemia, weaker bones, poor diet, and trauma. The value was estimated by the classification method using machine learning technologies. The patient's status of non-chronic kidney failure and chronic kidney failure will be estimated using classification models constructed using various classification techniques. These models were put to the test using a collection of data on chronic kidney illness from the University of California at Irvine, this had four hundred entries of data with twenty-five attributes. The outcomes of various models are examined. The model built with Decision tree method performed the best in terms of correctness using 14 attributes for the small dataset, based on the comparison. The final phase of this study will examine how effectively the machine learning system predicts chronic kidney failure with respect to precision, recall, accuracy, and F1-Score