A DATASET AT A GLANCE...

Do you know that March 14 of every year, since 2006, has been declared as the World Kidney Day? This day is particularly recognized to take care of kidneys, a more vital organ indulged in removing bodily toxins/ wastes, managing blood pressure and maintaining chemical balance, aiding red blood cell generation and many more vital functions. However, the poor lifestyle and the worst food choices in the current period has led to excessive weight gains and the incidence of a plethora of diseases. One among them is the Chronic Kidney Disease (CKD), which is expected to be the fifth global leading cause of death in 2040, as per an article in The Lancet. CKD is a progressive dysfunction of the kidneys, especially the functional units in it, called the nephrons. This disease has to be taken seriously, as it neither exhibits symptoms at earlier stages, nor be alleviated without effective treatment at final stages. Usually, blood and urine tests are the only promising ways to detect the disease at an earlier stage. Yet, more improved CKD risk classification and patient outcomes, proper planning of personalized medicine and intensive monitoring of high-risk patients with reduced healthcare costs are achievable only with the application of Machine Learning (ML) techniques. Researchers, attempting to classify the existence or the absence of CKD using novel ML approaches, can use the Chronic Kidney Disease dataset from the UC Irvine Machine Learning Repository (https://archive.ics.uci.edu/dataset/336/chronic+kidney+disease). This dataset encompasses 400 instances with 24 features each for CKD and non-CKD classification and they are: age, blood pressure, specific gravity, albumin, sugar, red blood cells, pus cell, pus cell clumps, bacteria, blood glucose random, blood urea, serum creatinine, sodium, potassium, hemoglobin, packed cell volume, white blood cell count, red blood cell count, hypertension, diabetes mellitus, coronary artery disease, appetite, pedal edema and anemia. The dataset also has a citation request as: “Soundarapandian, P., Rubini, L. J., & Eswaran, P. (2015). Chronic_Kidney_Disease Data Set. UCI Mach. Learn. Repository, School Inf. Comput. Sci., Univ. California, Irvine, CA, USA”.