Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Network

TL;DR

This paper presents a deep learning neural network with dropout layers to reduce overfitting in diabetes prediction, achieving superior performance on the Pima Indians Diabetes Data Set.

Contribution

It introduces a neural network model with dropout to specifically address overfitting in diabetes prognosis, outperforming existing methods.

Findings

Outperforms state-of-the-art methods on the Pima dataset

Dropout effectively reduces overfitting in the neural network

Achieves the best recorded accuracy for this dataset

Abstract

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.