Applying Average K Nearest Neighbour Algorithm to Detect Type-2 Diabetes

Thirumal, P. C.; Nagarajan, N.
May 2014
Australian Journal of Basic & Applied Sciences;May2014, Vol. 8 Issue 7, p128
Academic Journal
With the modern lifestyle and increasing intake of unhealthy diets causes obesity among adults and leads to diabetes mellitus. This has become a major health problem worldwide. Healthcare industries maintain large amount data about the patients, diseases, causes and medical devices. These large records serve as a source for the knowledge extraction and diagnosis of diseases. Many methods have been proposed to detect and prevent the diabetes. In this paper, we propose a data mining technique Average KNN algorithm to predict the people who are likely to get affected by the Type-2 Diabetes. Type-2 diabetes is caused when there is a high level of sugar in blood. The dataset used here is the Pima Indian Diabetes Dataset, which has the information of patients with diabetes and developing diabetes. There are other algorithms such as Bayesian Classifier (BC) and decision tree were proposed recently. On the other hand KNN is a non-parametric lazy algorithm with the advantage over the assumption of the data distribution. So methods have been proposed and implemented to make KNN a faster algorithm. Weka software is used for this study. Results shown that the proposed system predicts the people getting chances of affected with type-2 diabetes.


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