Chronic Kidney Disease Research Paper

ABSTRACT Chronic kidney disease refers to the kidneys have been damaged by conditions, such as diabetes, glomerulonephritis or high blood pressure. Kidney disease also makes more possible to mature heart and blood vessel disease. These problems may happen gently on 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 deferral of these complications. One of the main challenges is giving proper treatment and accurate diagnosis of the disease. The major task is to find an accurate algorithm which doesn’t require long time to run for giving accurate and correct results. The aim of this

CKD is a long term disorder caused by damage to both kidneys [1], [2]. There is no single cause and the damage is typically permanent and can lead to ill health. In some cases dialysis or transplantation may become essential. Diabetes mellitus is also becoming more common in one cause of CKD. Chronic kidney disease is become more frequently in older people and consequently is likely to increase in the population as a whole. People with chronic kidney diseases are at higher risk of cardiovascular disease and they should be recognized early so that appropriate preemptive measures can be taken [4]. In the early stages of CKD people may be unaware that they have some illness so the blood or urine test may be an only way to discover the disease. Establishing which conditions influence to CKD identifies those who should have the required blood or urine tests. Early finding of CKD can establish if kidney disease is likely to be liberal allowing appropriate treatment to slow

A Naive Bayesian model is easy to build, with no complex iterative parameter assessment which makes it especially useful for very large datasets. Even though it’s simple, the Naive Bayesian classifier often does unexpectedly well and is widely used because it often outperforms more refined classification methods. Bayes theorem delivers a way of calculating the posterior probability, P(c|x), from P(c), P(x), and P(x|c). Naive Bayes classifier assumes that the effect of the value of a predictor (x) on a given class (c) is independent of the values of other predictors. This hypothesis is called class conditional independence