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Erschienen in:
Image-Based Content Retrieval via Class-Based Histogram Comparisons Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Network

verfasst von : Akm Ashiquzzaman, Abdul Kawsar Tushar, Md. Rashedul Islam, Dongkoo Shon, Kichang Im, Jeong-Ho Park, Dong-Sun Lim, Jongmyon Kim

Erschienen in: IT Convergence and Security 2017

Verlag: Springer Singapore

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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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Vorheriges Kapitel Improving Classification of Imbalanced Student Dataset Using Ensemble Method of Voting, Bagging, and Adaboost with Under-Sampling Technique
Nächstes Kapitel An Improved SVM-T-RFE Based on Intensity-Dependent Normalization for Feature Selection in Gene Expression of Big-Data
Literatur
1.
Alberti, K.G.M.M., Zimmet, P.F.: Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus. In: provisional report of a WHO consultation. Diabetic Med. 15(7), 539–553 (1998)CrossRef
2.
National Diabetes Data Group: Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance. Diabetes 28(12), 1039–1057 (1979)CrossRef
3.
Vashist, S.K.: Non-invasive glucose monitoring technology in diabetes management: A review. Anal. Chim. Acta 750, 16–27 (2012)CrossRef
4.
Potts, R.O., Tamada, A.J., Tierney, J.M.: Glucose monitoring by reverse iontophoresis. Diabetes Metab. Res. Rev. 18, S49–S53 (2002)CrossRef
5.
Wentholt, I.M.E., Hoekstra, J.B.L., Zwart, A., DeVries, J.H.: Pendra goes Dutch: lessons for the CE mark in Europe. Diabetologia 48(6), 1055–1058 (2005)CrossRef
6.
Harman-Boehm, I., Gal, A., Raykhman, A.M., Zahn, J.D., Naidis, E., Mayzel, Y.: Noninvasive glucose monitoring: a novel approach. J. Diabetes Sci. Technol. 3(2), 253–260 (2009)CrossRef
7.
Bandodkar, A.J., Jia, W., Yardımcı, C., Wang, X., Ramirez, J., Wang, J.: Tattoo-based noninvasive glucose monitoring: a proof-of-concept study. Anal. Chem. 87(1), 394–398 (2014)CrossRef
8.
Lee, H.J., Choi, T.K., Lee, Y.B., Cho, H.R., Ghaffari, R., Wang, L., Choi, H.J., Chung, T.D., Lu, N., Hyeon, T., Choi, S.H., Kim, D.H.: A graphene-based electrochemical device with thermoresponsive microneedles for diabetes monitoring and therapy. Nat. Nanotechnol. 11(6), 566–572 (2016)CrossRef
9.
Zanon, M., Sparacino, G., Facchinetti, A., Talary, M.S., Mueller, M., Caduff, A., Cobelli, C.: Non-invasive continuous glucose monitoring with multi-sensor systems: a Monte Carlo-based methodology for assessing calibration robustness. Sensors 13(6), 7279–7295 (2013)CrossRef
10.
Caduff, A., Zanon, M., Mueller, M., Zakharov, P., Feldman, Y., De Feo, O., Donath, M., Stahel, W.A., Talary, M.S.: The effect of a global, subject, and device-specific model on a noninvasive glucose monitoring multisensor system. J. Diabetes Sci. Technol. 9(4), 865–872 (2015)CrossRef
11.
Park, Y.J., Seong, K.E., Jeong, S.Y., Kang, S.J.: Self-organizing wearable device platform for assisting and reminding humans in real time. Mobile Inform. Syst. 2016, 15 (2016)
12.
Smith, J.W., Everhart, J., Dickson, W., Knowler W., Johannes, R.: Using the adap learning algorithm to forecast the onset of diabetes mellitus. In: Proceedings of the annual symposium on computer application in medical care. American Medical Informatics Association, p. 261 (1988)
13.
Kayaer, K., Yildirim, T.: Medical diagnosis on pima indian diabetes using general regression neural networks. In: Proceedings of the International Conference on Artificial Neural Networks and Neural Information Processing, pp. 181–184 (2003)
14.
Ashiquzzaman, A., Tushar, A. K.: Handwritten arabic numeral recognition using deep learning neural networks. In: 2017 IEEE International Conference on Imaging, Vision & Pattern Recognition (icIVPR), pp. 1–4. IEEE (2017)
15.
Dasgupta, J., Sikder, J., Mandal, D.: Modeling and optimization of polymer enhanced ultrafiltration using hybrid neuralgenetic algorithm based evolutionary approach. Appl. Soft Comput. 55, 108–126 (2017)CrossRef
16.
17.
Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)MathSciNetMATH
18.
Krizhevsky, A., Sutskever, I., and Hinton, G. E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
19.
Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: 27th International Conference on Machine Learning, pp. 807–814 (2010)
20.
Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. Aistats 15(106), 275 (2011)
21.
Heaton, J.: Introduction to neural networks with Java. Heaton Research, Inc. (2008)
22.
Panchal, G., Ganatra, A., Kosta, Y., Panchal, D.: Review on methods of selecting number of hidden nodes in artificial neural network. Int. J. Comput. Theory Eng. 3(2), 332–337 (2011)CrossRef
23.
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:​1207.​0580 (2012)
24.
Warde-Farley, D., Goodfellow, I. J., Courville, A., Bengio, Y.: An empirical analysis of dropout in piecewise linear networks. arXiv preprint arXiv:​1312.​6197 (2013)
25.
26.
27.
Theano Development Team, Theano: a Python framework for fast computation of mathematical expressions. arXiv e-prints, vol. abs/1605.02688 (2016)
28.
Metadaten
Titel
Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Network
verfasst von
Akm Ashiquzzaman
Abdul Kawsar Tushar
Md. Rashedul Islam
Dongkoo Shon
Kichang Im
Jeong-Ho Park
Dong-Sun Lim
Jongmyon Kim
Copyright-Jahr
2018
Verlag
Springer Singapore
Buch
IT Convergence and Security 2017

Print ISBN: 978-981-10-6450-0

Electronic ISBN: 978-981-10-6451-7

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-981-10-6451-7_5