Prediction Of Diabetes Using Soft Computing Techniques- A Survey
M. Durairaj, G. Kalaiselvi
Index Terms: Artificial Neural Network (ANN), C4.5 Classifier, Support Vector Machine (SVM), K-Nearest Neighbour (KNN).
Abstract: Neural Networks are one of the soft computing techniques that can be used to make predictions on medical data. Neural Networks are known as the Universal predictors. Diabetes mellitus or simply diabetes is a disease caused due to the increase level of blood glucose. Various traditional methods, based on physical and chemical tests, are available for diagnosing diabetes. The Artificial Neural Networks (ANNs) based system can effectively applied for high blood pressure risk prediction. This improved model separates the dataset into either one of the two groups. The earlier detection using soft computing techniques help the physicians to reduce the probability of getting severe of the disease. The data set chosen for classification and experimental simulation is based on Pima Indian Diabetic Set from (UCI) Repository of Machine Learning databases. In this paper, a detailed survey is conducted on the application of different soft computing techniques for the prediction of diabetes. This survey is aimed to identify and propose an effective technique for earlier prediction of the disease.
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