Predicting Diabetics Accuracy Using Rough Set Clusters
Shantan Sawa, H. Balaji, N. Ch. S. N. Iyengar, Ronnie D. Caytiles · International Journal of Grid and Distributed Computing · 2017
The primary objective of this paper is to develop and propose a model using the concepts from Rough Set Theory to cluster the patients in the diabetic dataset.The model to be developed incorporates Rough Clustering of the dataset, and from the clusters formed, compute the accuracy on the testing data.Rough Clustering will help splitting the data into clusters of patients that suffer from Diabetes Mellitus and the ones which do not.As a result, the patients suffering from Diabetes Mellitus will be clustered together and will provide us with the average values of the features used in the model for data clustering.The results obtained will provide more depth in the field of rough clustering for diabetes as the number of studies done on diabetes using rough set theory are few to none.