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.
@article{arxiv.1707.08386,
title = {Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Network},
author = {Akm Ashiquzzaman and Abdul Kawsar Tushar and Md. Rashedul Islam and Jong-Myon Kim},
journal= {arXiv preprint arXiv:1707.08386},
year = {2017}
}
Comments
8 pages, 3 Figures, 3 Tables; Conference - 7th iCatse International Conference on IT Convergence and Security, 2017 (http://icatse.org/icitcs2017/) (accepted)