  Augmented accuracy in prediction of diabetes will open up new frontiers in
health prognostics. Data overfitting is a performance-degrading issue in
diabetes prognosis. In this study, a prediction system for the disease of
diabetes is pre-sented where the issue of overfitting is minimized by using the
dropout method. Deep learning neural network is used where both fully connected
layers are fol-lowed by dropout layers. The output performance of the proposed
neural network is shown to have outperformed other state-of-art methods and it
is recorded as by far the best performance for the Pima Indians Diabetes Data
Set.
