International Journal of Computer Applications Technology and Research Volume 3– Issue 9, 554 - 558, 2014, ISSN: 2319–8656 www.ijcat.com 554 Comparative Study of Diabetic Patient Data’s Using Classification Algorithm in WEKA Tool P.Yasodha Pachiyappa's college for women, Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya Kanchipuram, India N.R. Ananthanarayanan Pachiyappa's college for women, Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya Kanchipuram, India Abstract: 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. Keywords- Data Mining, Diabetics data, Classification algorithm, Weka tool 1. INTRODUCTION The main focus of this paper is the classification of different types of datasets that can be performed to determine if a person is diabetic. The solution for this problem will also include the cost of the different types of datasets. For this reason, the goal of this paper is classifier in order to correctly classify the datasets, so that a doctor can safely and cost effectively select the best datasets for the diagnosis of the disease. The major motivation for this work is that diabetes affects a large number of the world population and it’s a hard disease to diagnose. A diagnosis is a continuous process in which a doctor gathers information from a patient and other sources, like family and friends, and from physical datasets of the patient. The process of making a diagnosis begins with the identification of the patient’s symptoms. The symptoms will be the basis of the hypothesis from which the doctor will start analyzing the patient. This is our main concern, to optimize the task of correctly selecting the set of medical tests that a patient must perform to have the best, the less expensive and time consuming diagnosis possible. A solution like this one, will not only assist doctors in making decisions, and make all this process more agile, it will also reduce health care costs and waiting times for the patients. This paper will focus on the analysis of data from a data set called Diabetes data set. 2. RELATED WORK The few medical data mining applications as compared to other domains. [4] Reported their experience in trying to automatically acquire medical knowledge from clinical databases. They did some experiments on three medical databases and the rules induced are used to compare against a set of predefined clinical rules. Past research in dealing with this problem can be described with the following approaches: (a) Discover all rules first and then allow the user to query and retrieve those he/she is interested in. The representative approach is that of templates [3]. This approach lets the user to specify what rules he/she is interested as templates. The system then uses the templates to retrieve the rules that match the templates from the set of discovered rules. (b) Use constraints to constrain the mining process to generate only relevant rules. [12] Proposes an algorithm that can take item constraints specified by the user in the association rule mining processor that only those rules that satisfy the user specified item constraints are generated. The study helps in predicting the state of diabetes i.e., whether it is in an initial stage or in an advanced stage based on the characteristic results and also helps in estimating the maximum number of women suffering from diabetes with specific characteristics. Thus patients can be given effective treatment by effectively diagnosing the characteristics. Our research work based on the concept from Data Mining is the knowledge of finding out of data and producing it in a form that is easily understandable and comprehensible to humans in general. These further extended in this to make an easier use of the data’s available with us in the field of Medicine. The main use of this technique is the have a robust working model of this technology. The process of designing a model helps to identify the different blood groups with available Hospital Classification techniques for analysis of Blood group data sets. The ability to identify regular diabetic patients will enable to plan systematically for organizing in an effective manner. Development of data mining technologies to predict treatment errors in populations of patients represents a major advance in patient safety research.