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Conference Paper Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Network
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Authors
Akm Ashiquzzaman, Abdul Kawsar Tushar, Md. Rashedul Islam, Dongkoo Shon, Kichanf Im, Jeong-Ho Park, Dong-Sun Lim, Jongmyon Kim
Issue Date
2017-09
Citation
International Conference on IT Convergence and Security (ICITCS) 2017 (LNEE 449), v.449, pp.35-43
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1007/978-981-10-6451-7_5
Project Code
17ZS1700, The development of smart HSE system in ship and plant building yard , Jang Byungtae
Abstract
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.
KSP Keywords
Accurate prediction, Data sets, Learning neural network, Prediction System, Prediction accuracy, State-of-art, deep learning(DL), diabetes prediction