Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Network
This work addresses data overfitting for improved diabetes prognosis, which is an incremental advance in health prognostics.
The study tackled overfitting in diabetes prediction by using a deep learning neural network with dropout layers, achieving the best performance on the Pima Indians Diabetes Data Set compared to state-of-the-art methods.
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.