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.
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Acknowledgements
This work was supported by Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government [17ZS1700, Development of smart HSE system for shipyard and onshore plant]. The authors also acknowledge department of Computer Science and Engineering, University of Asia Pacific for supporting this research in various ways.
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Ashiquzzaman, A. et al. (2018). Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Network. In: Kim, K., Kim, H., Baek, N. (eds) IT Convergence and Security 2017. Lecture Notes in Electrical Engineering, vol 449. Springer, Singapore. https://doi.org/10.1007/978-981-10-6451-7_5
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DOI: https://doi.org/10.1007/978-981-10-6451-7_5
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