Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Network

被引：41

作者：

Ashiquzzaman, Akm ^{[1
]}

Tushar, Abdul Kawsar ^{[1
]}

Islam, Md. Rashedul ^{[1
]}

Shon, Dongkoo ^{[4
]}

Im, Kichang ^{[4
]}

Park, Jeong-Ho ^{[3
]}

Lim, Dong-Sun ^{[3
]}

Kim, Jongmyon ^{[2
]}

机构：

[1] Univ Asia Pacific, Dept CSE, Dhaka, Bangladesh

[2] Univ Ulsan, Dept Elect Elect & Comp Engn, Ulsan, South Korea

[3] ETRI, Intelligent Robot Res Div, Ind IT Convergence Res Grp, SW Contents Res Lab, Daejeon, South Korea

[4] Univ Ulsan, Safety Ctr, Ulsan, South Korea

来源：

IT CONVERGENCE AND SECURITY 2017, VOL 1 | 2018年 / 449卷

关键词：

Dropout; Healthcare; Data overfitting; Diabetes prediction; Neural network; Deep learning;

D O I：

10.1007/978-981-10-6451-7_5

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Accurate prediction of diabetes is an important issue in health prognostics. However, data overfitting degrades the prediction accuracy in diabetes prognosis. In this paper, a reliable prediction system for the disease of diabetes is presented using a dropout method to address the overfitting issue. In the proposed method, deep learning neural network is employed where fully connected layers are followed by dropout layers. The proposed neural network outperforms other state-of-art methods in better prediction scores for the Pima Indians Diabetes Data Set.

引用

页码：35 / 43

页数：9

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