Now a days Diabetes mellitus is a major global public health problems. The machine learning techniques can be applied to help the people in detection of diabetes at an early stage and treatment, which may help in avoiding complications. In our work attempts to propose three kinds of techniques K- Nearest Neighbor (KNN), Naive Bayes (NB) and Artificial Neural Network (ANN) for classifying the individual user as diabetic or non diabetic.Providing diagnostic aid for diabetic by using a set of data that contains only medical information obtained without advanced medical equipments, can help number of people who want to discover the disease or the risk of disease at an initial stage. The experimental system achieves classification accuracy of KNN is 92.59%, NB is 85.71% and ANN is 94.64%. The aim of this study is to classify diabetes disease and deploy in to cloud for cost effective and easy to use.

Diabetes mellitus, K-Nearest Neighbor, Naïve Bayes, Artificial Neural Network, Cloud

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