The race for urbanization and thirst for high living status leads to unhealthy life. As the result a rapid growth in number of diabetic patients in urban areas approaching to its deadline. In this situation it become a prime necessity for physicians and health workers to recognize accurate growth rate in number of diabetic patients. Artificial Neural Network is used as one of the artificial intelligent technique for forestalling growth rate of type II diabetic patients. Diabetes occurred due to increased level of glucose in blood. In this paper, an intense survey is done for the prediction of Type II diabetes using different Data Mining tools and Artificial Neural Network techniques, is presented. This survey is aimed to recognize and propose an effective technique for earlier prediction of the Type II diabetes. The data mining techniques like C4.5 Classifier, Support Vector Machine and K-Nearest Neighbour are compared for this work with Artificial Neural Network. As the results Artificial Neural Network found with a great accuracy of 89%.
Diabetes is a serious and progressive condition that is rapidly increasing in incidence and currently ranks third on the list of all causes of mortality throughout the globe. A key challenge for any nation, but particularly for one that is undergoing substantial change is the high diabetes prevalence rate. Research in the field of epidemiology has demonstrated that obesity and Type II diabetes are the result of a combination of genetic predisposition and lifestyle factors such as bad eating habits and a lack of physical exercise. This article presents machine learning and feature selection enabled framework for diabetes type 2 prediction. This article uses artificial neural network for classification and prediction of diabetes type 2 data. Input data used in experiment is gathered from Pima Indian Diabetes Dataset. Results are compared on the basis of certain parameters like- accuracy, sensitivity, specificity. Accuracy of artificial neural network is better for classification and prediction of type 2 diabetes.
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