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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
- 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