International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 10 | Oct -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 1664 A Review on Naive Baye’s ȋNBȌ, J48 and K-Means Based Mining Algorithms for Medical Data Mining Rajbir Kaur 1 , Rakesh Gangwar 2 , 1 M.Tech Scholar, Department of Computer Science & Engineering Beant College of Engineering and Technology, Gurdaspur, Punjab, India 2 Associate Professor, Department of Computer Science & Engineering Beant College of Engineering and Technology, Gurdaspur, Punjab, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Data mining can be defined as discovery of meaningful patterns of large quantity of data and it analyze and explore to data . This paper studies various data mining techniques for improve accuracy rate for prediction of various diseases. This paper reviews the techniques and various evaluation techniques that describe and distinguish various kind of techniques for detection of diseases and their treatment in medical data mining. Key Words: Data Mining techniques, Naive bayes, ANN, KNN 1. INTRODUCTION Data Mining is one of the very motivating and critical part of study with desire to of removing data from significant amount of accumulated information sets..An transformative route has been experienced in the repository market in the progress of the next functionalities information col lection and repository formation, information management (including information storage and collection, and repository purchase processing), and information analysis and understanding (involving data warehousing and data mining). Merely said, information mining refers to removing or \mining" know corner from big amounts of data.We've been collecting a myriad of information, from easy exact proportions and text documents, to more complicated data such as for instance spatial information, multimedia programs, and hypertext documents. Information Mining, also generally known as Knowledge Discovery in Sources (KDD), refers to the nontrivial extraction of implicit, previously as yet not known and possibly of good use data from information in databases. While data mining and understanding discovery in sources (or KDD) are often treated as synonyms, data mining is clearly part of the understanding discovery method 2. DATA MINING TECHNIQUES It is the process of turning raw data into useful information so that various pattern can be extracted. Various researchers have studied and work on data mining techniques to evaluate and classify the diseases for medical data 2.1 ANN (Artificial Neural Network) ANN is a classification model which is grouped by interconnected nodes. It can be viewed as a circular node which is represented as an artificial neuron that reveals the output of one neuron to the input of another. The ANN model is helpful in revealing the hidden relationships in the historical data, thus facilitating the prediction and forecasting of diseases of patients.ANN model is accurate enough to make important and relevant decisions regarding data usage. 2.2 NAIVE BAYES Naïve Bayes is a classification technique which is based on probability theories which fully embody the characteristics of data of medical science. Bayes model is easy to use for very large datasets. In simple terms, a Naive Bayes assumed that the value of a particular feature does not related to the presence or absence of any other feature, given in the class variable. It undergoes through following steps: a) Extract, clean and classify the symptoms of diseases. b) Remove large punctuations and split them. c) Counting Tokens and calculating the probability. This probability is called as posterior probability which is calculated by the formula described in. d) Adding the probabilities and then wrapping up. 2.3 DECISION TREE Decision tree is one of the predictive modeling technique used in data mining. It aids to divide the larger dataset into smaller dataset indicating a parent-child relationship. Each internal node is labeled with an input feature. Different kind of attribute test are express by internal nodes, test result are represent by bifurcations and nodes of leaf express classification of that type. Decision tree can handle both numerical and categorical data. It is well suited with large datasets. Higher accuracy in decision tree classification technique depicts that the technique can simulate. Decision tree is able to deal and handle large quantity of input data such as text with numeric data only textual or nominal. It is a