Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Network

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 presented where the issue of overfitting is minimized by using the dropout method. Deep learning neural network is used where both fully connected layers are followed 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.