A Hybrid Approach for Prediction of Chronic Kidney Diseases using Data Mining Techniques

Abstract

ABSTRACT newlineAmple data are collected by the healthcare industry which can be mined and classified for efficient detection and diagnosis of certain diseases. These healthcare data are to be preprocessed for efficient detection of diseases and also for arriving at conclusive decisions. This situation can be better handled by latest techniques of data mining, several techniques were introduced to extract and preprocess these healthcare data efficiently. The practice of examining large preexisting database to generate new information is termed as data mining. Various data mining techniques can be used in the process of arriving decisions. newlineThe dataset are examined and pre-processed for solving the problem of missing attribute values in the dataset. The optimal selection of the features and efficient classification must follow to make the process well organized. By using efficient techniques of data mining, goal of this research work is to foresee kidney disease in the patients. The quality of pre-processing or at any other stage had to face minor hindrances due to some of the techniques introduced during the process. newlineThe major problems are explained below and innovative techniques are proposed to solve the problems. In many cases, an imprecise diagnosis and extensively organized medical procedures may lead to many difficulties for the patient health. It is advisable to newlineii newlinego for early diagnosis and prediction of kidney disease as it may to prolong survival rate among patients. Missing values in the heterogeneous dataset from different sources lead to problem in diagnosing process. Feature selection also plays the vital role in early prediction. Since many feature selection process are available, a better method has to be chosen to carry out the selection process easily and efficiently. newline newline