PERFORMANCE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR PREDICTING CHRONIC KIDNEY DISEASE

Abstract

chronic kidney disease refers to the condition of kidneys caused by conditions, diabetes, glomerulonephritis or high blood pressure. These problems may happen gently for a long period of time, often without any symptoms. It may eventually lead to kidney failure requiring dialysis or a kidney transplant to preserve survival time. So, the primary detection and treatment can prevent or delay of these complications. The aim of this work is to reduce the diagnosis time and to improve the diagnosis accuracy through classification algorithms. The proposed work deals with classification of different stages in chronic kidney diseases using machine learning algorithms. The experimental results performed on different algorithms like Naive Bayes, Decision Tree, K-Nearest Neighbor and Support Vector Machine. The experimental result shows that the K-Nearest Neighbor algorithm gives better result than the other classification algorithms and produces 98% accuracy