Comparative Study of Diabetic Patient Data’s Using Classification Algorithm in WEKA Tool
P. Yasodha, N.R. Ananthanarayanan · International Journal of Computer Applications Technology and Research · 2014
Data mining refers to extracting knowledge from large amount of data.Real life data mining approaches are interesting because they often present a different set of problems for diabetic patient's data.The research area to solve various problems and classification is one of main problem in the field.The research describes algorithmic discussion of J48, J48 Graft, Random tree, REP, LAD.Here used to compare the performance of computing time, correctly classified instances, kappa statistics, MAE, RMSE, RAE, RRSE and to find the error rate measurement for different classifiers in weka .In this paper the data classification is diabetic patients data set is developed by collecting data from hospital repository consists of 1865 instances with different attributes.The instances in the dataset are two categories of blood tests, urine tests.Weka tool is used to classify the data is evaluated using 10 fold cross validation and the results are compared.When the performance of algorithms, we found J48 is better algorithm in most of the cases.