International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 05 | May-2018 www.irjet.net p-ISSN: 2395-0072 © 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 676 VARIOUS DATA MINING TECHNIQUES ANALYSIS TO PREDICT DIABETES MELLITUS Mr. R. Sengamuthu 1 , Mrs. R. Abirami 2 , Mr. D. Karthik 3 1 ,2, 3 Assistant Professor, Department of Computer Science, Govt Arts College , Ariyalur. ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Data mining approach helps to diagnose patient’s diseases. Diabetes Mellitus is a chronic disease to affect various organs of the human body. Early prediction can save human life and can take control over the diseases. This paper explores the early prediction of diabetes using various data mining techniques. The dataset has taken 768 instances from PIMA Indian Dataset to determine the accuracy of the data mining techniques in prediction. The analysis proves that Modified J48 Classifier provide the highest accuracy than other techniques. Key Words: Data mining, Diabetes, Prediction, accuracy, classification 1. INTRODUCTION Today the buzz word is DzHealth Caredz all over the world. Early Prediction of diseases can reduce the fatal rate of human. There are very large and enormous data available in hospitals and medical related institutions. Information technology plays a vital role in Health Care. Diabetes is a chronic disease with the potential to cause a worldwide Health Care crisis. According to International Diabetes Federation 382 million people are living with diabetes world wide. By 2035, this will be doubled as 592 million. Early prediction of diabetes is quite challenging task for medical practitioners due to complex interdependence on various factors. Diabetes affects human organs such as kidney, eye, heart, nerves, foot etc. Data mining is a process to extract useful information from large database. It is a multidisciplinary field of computer science which involves computational process, machine learning, statistical techniques, classification, clustering and discovering patterns. Data mining techniques has proved for early prediction of disease with higher accuracy inorder to save human life and reduce the treatment cost. This paper explores various Data mining techniques such as Navie Bayes, MLP, Bayesian Network, C4.5, Amalgam KNN, ANFIS, PLS-LDA, Homegenity- Based, ANN, Modified J48 etc. are analyzed to predict the diabetes disease. Veena 2014 combined AmalgamKNN and ANFIS to improve the accuracy in prediction. In this K-means and KNN are combined to overcome the computational complexity of large number of dataset. And the training set is verified with fuzzy systems and neural networks to produce better result. Sapna 2012 implemented genetic algorithm with data mining techniques to test the patients affected by diabetes based upon the fitness value and the accuracy chromosome value. Gaganjot Kaur 2014 proposed a new approach for predicting the diabetes using WEKA and MATLAB for generating J48 classifiers with improved existing J48 algorithm. Murat Koklu 2013 formed a decision support system using data mining and artificial intelligence classification algorithms namely Multilayer Perceptron, Navie Bayes classification and J48 to diagnose illness. To achieve good performances in predicting the onset of diabetes, Manaswini Pradhan 2011 suggested and experimented ANN based classification model and Genetic algorithm for feature selection. Hence, this paper mainly focused on Data mining techniques and analyzed its accuracy with various tools. Diabetes Diabetes Mellitus (DM) is commonly referred as Diabetes; it is the condition in which the body does not properly process food for use as energy. Most of the food we eat is turned into glucose or sugar for energy. The pancreas, an organ makes a hormone called insulin to help glucose get into the cells of our bodies. When a body is affected with diabetes, it couldn’t make enough insulin or couldn’t use its own insulin. This causes sugar to build up into blood. Several pathogenic processes are involved in the development of diabetes. These range from autoimmune destruction of the β-cells of the pancreas with consequent insulin deficiency to abnormalities that result in resistance to insulin action. Diabetes is a life threatening disease in rural and urban, then developed and under developed countries. The common symptoms for the diabetic patients are frequent urination, increased thirst, weight loss, slow-healing in wound, giddiness, increased hunger etc. Diabetes can cause serious health complications including heart disease, blindness, kidney failure and low-extremity amputations. A. Types of Diabetes Type 1 Diabetes is called insulin-dependent diabetes mellitus (IDDM) or juvenile-onset diabetes. Autoimmune, genetic, and environmental factors are involved in the development of this type of diabetes. Type1 mostly occurs in young people who are below 30 years. This type can affect children or adults, but majority of these diabetes cases were in children. In persons with type 1 diabetes, the beta cells of the pancreas, which are responsible for insulin production, are destroyed due to autoimmune system. Type 2 Diabetes is called non-insulin-dependent diabetes mellitus (NIDDM) or adult-onset diabetes. In the type 2 diabetes, the pancreas usually produces some insulin the amount produced is not enough for the body's needs, or the