Analyzing Diabetic Data using Classification Algorithms in Data Mining

Backgrounds/Objectives: Huge medical datasets available in various data repositories which are used for real world applications. To visualize the useful information stored in data warehouses, the Data Mining (DM) methods are enormously utilized. One of such domain is medical domain, in which the function of DM approach raises speedy recovery of sickness over indications. On the way to categorize and predict symptoms in medicinal data, a variety of DM methods are utilized by different researchers. From many techniques of DM, classification is one of the main techniques. The classification techniques classify the unseen information in all areas including medical diagnostic field. The very dangerous disease in medicinal field is diabetes disease which is affected for many peoples in popular countries like India. Methods/Statistical Analysis: The impact of categorization is very important in authentic earth applications in all fields. To categorize the rudiments allowing to the applications of the elements during the predefined set of modules are used by classification methods. Very popular classification algorithms J48, Support Vector Machines (SVM), Classification and Regression Tree CART and k-Nearest Neighbor (kNN) for diabetic data are used for this research work. Findings: To discover the presentation of these classification methods, diabetic data as an input. For the most part, this research work is supported out to associate the techniques in the calculation of the presentation accurateness in diabetic data. The above mentioned techniques are used for diabetic data to categorize its accuracy in terms of its performance. Methods: The conclusion of this research work is choosing the top algorithm for the input data for the best classifier. Applications/Improvements: Some of other algorithms are analyzed using the same data set for the similar type of results is discussed in future. Also, some of the clustering algorithms are applied using the same data set to find highly affected diabetic patients.