In this paper, various data mining techniques are analyzed and their proficiencies have been evolved. Medical professionals need a reliable prediction methodology to diagnose factors influencing diabetes. There are large quantities of information about patients and their medical conditions. Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information. The main aim of this thesis is to show the comparison of different classification algorithms such as Multilayer perceptron neural network (MLPNN), Zero R, K-Star based on computing time, Correctly Classified Instances, Incorrectly Classified Instances, Mean absolute error, Root mean squared error, Relative absolute error, Root relative squared error, precision value , Recall , F-measure, the data evaluated using 25 fold Cross Validation error rate, error rate focuses True Positive, True Negative, False Positive and False Negative and the clustering algorithm such as K-means algorithm based on varied number of clusters and Sum of squared error. Classification is an important data mining technique with broad applications. It classifies data of various kinds. Classification is used in every field of our life. Classification is used to classify each item in a set of data into one of predefined set of classes or groups. Clustering analysis method is one of the main analytical methods in data mining; in which k-means clustering algorithm is most popularly/widely used for many applications. K-means algorithm has higher efficiency and scalability and converges fast when dealing with large data sets. Clustering is an adaptive procedure in which objects are clustered or grouped together, based on the principle of maximizing the intra-class similarity and minimizing the inter-class similarity. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Waikato Environment for Knowledge Analysis or in short, WEKA is used to obtain the accuracy of algorithms and find out which algorithm is most suitable for user working on data of diabetic patients. Weka is a data mining tool. It contains many machine leaning algorithms. It provides the facility to classify our data through various algorithms.

Multilayer perceptron neural network (MLPNN), Zero R, K-Star, K-means, WEKA

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