• Dr. M.Manimekalai
• S. Divya

Diabetes is one of the major diseases of the population across the world. Diabetes is a chronic disease that occurs either when the pancreas does not produce enough insulin or when the body cannot efficiently use the insulin it produces. In 2014, 8.5% of adults aged 18 years and older had diabetes. In 2012, diabetes was the direct cause of 1.5 million deaths and high blood glucose was the cause of another 2.2 million deaths. Over the time, diabetes can damage the heart, blood vessels, eyes, kidneys, and nerves. Early diagnosis can be made through a relatively inexpensive method of computation. In this paper, Machine Learning, a branch of Artificial Intelligence is used to analyze and make the diabetes prediction model. Various researchers have also been done to predict the diabetes machine learning algorithm, but this is an additional effort in the research work based on a specific type of patient in a specific community. In this research work, a data sample of Pima Indians was taken to predict the possibility of diabetes. Among several algorithms of Machine learning, Artificial Neural Network (ANN) was chosen for building the model to predict diabetes. This model is ideal for predicting the possibility of diabetes with 92% accuracy while tested with the sample test data. This model can achieve more accuracy if it is trained with a large sample training data in the future.

Diabetes, Data Mining Techniques, Feature Selection, Classification, Artificial Neural Networks, Machine Learning.

"Prediction of Diabetes using Artificial Neural Network Classification Technique", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.9-13, June 2019, Available :http://www.jetir.org/papers/JETIRDB06002.pdf

"Prediction of Diabetes using Artificial Neural Network Classification Technique", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp9-13, June 2019, Available at : http://www.jetir.org/papers/JETIRDB06002.pdf