查看文章

ince, classification is the most commonly applied data mining technique, and employs a set of pre-classified examples to develop a model that can classify the population of records at large. The major goal of the classification technique is to predict the target class accurately for each case in the data. The present study is focused on the usage of classification techniques in the field of medical science and bioinformatics. The main focus of this paper is Chronic-Kidney-Disease prediction using weka data mining tool and its usage for classification in the field of medical bioinformatics. It firstly classifies dataset and then determines which algorithm performs better for diagnosis and prediction of Chronic-Kidney-Disease. Prediction begins with identification of symptoms in patients and then identifying sick patients from a lot of sick and healthy ones. Thus, the prime objective of this paper is to analyze the data from a Chronic-Kidney-Disease dataset using classification technique to predict class accurately in each case. Many authors have used WEKA tool in their work to compare the performance of different classifiers applied on various datasets. But, none of the author worked on prediction of accuracy for chronic-kidney-disease dataset. Here, we have considered six number of classifiers to study their performance based on various parameters obtained by applying them in the dataset.