Abstract
Data mining is a growing discipline in the medical field that aims to extract knowledge relevant large amounts of data. It uses tools from statistics, artificial intelligence, and optimization techniques, etc. This paper present the detection of diabetes on the basis of data taken form UCI repository (PIMA), with help of neural network and principal component analysis. Data training and testing perform according to k fold verification and NN based approach yields 99% of accuracy. Further PCA NN approach is proposed for dimension reduction techniques and it gives accuracy 98.7% marginally low from NN based approach.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReference
Zeki, T.S., Malakooti, M.V., Ataeipoor, Y., Tabibi, S.T.: An expert system for diabetes diagnosis. Am. Acad. Sch. Res. J. 4(5), 1 (2012)
American Diabetes Association: Classification and diagnosis of diabetes. Diabetes Care 38(Suppl. 1), S8–S16 (2015)
Lavery, L.A., Armstrong, D.G., Murdoch, D.P., Peters, E.J., Lipsky, B.A.: Validation of the infectious diseases society of America’s diabetic foot infection classification system. Clin. Infect. Dis. 44(4), 562–565 (2007)
American Diabetes Association: Standards of medical care in diabetes—2014. Diabetes Care 37(Suppl. 1), S14–S80 (2014)
Amato, F., et al.: Artificial neural networks in medical diagnosis. J. Appl. Biomed. 11(2), 47–58 (2013)
Jayalakshmi, T., Santhakumaran, A.: A novel classification method for diagnosis of diabetes mellitus using artificial neural networks. In: 2010 International Conference on Data Storage and Data Engineering (DSDE), pp. 159–163. IEEE, February 2010
Ahmadlou, M., Adeli, H.: Enhanced probabilistic neural network with local decision circles: a robust classifier. Integr. Comput.-Aided Eng. 17(3), 197–210 (2010)
Karegowda, A.G., Manjunath, A.S., Jayaram, M.A.: Application of genetic algorithm optimized neural network connection weights for medical diagnosis of pima Indians diabetes. Int. J. Soft Comput. 2(2), 15–23 (2011)
Iyer, A., Jeyalatha, S. Sumbaly, R.: Diagnosis of diabetes using classification mining techniques, arXiv preprint (2015). arXiv:1502.03774
Durairaj, M., Kalaiselvi, G.: Prediction of diabetes using soft computing techniques-a survey. Int. J. Sci. Technol. Res. 4(3), 190–192 (2015)
Erkaymaz, O., Ozer, M.: Impact of small-world network topology on the conventional artificial neural network for the diagnosis of diabetes. Chaos, Solitons Fractals 83, 178–185 (2016)
Erkaymaz, O., Ozer, M., Perc, M.: Performance of small-world feedforward neural networks for the diagnosis of diabetes. Appl. Math. Comput. 311, 22–28 (2017)
Mahajan, A., Kumar, S. Bansal, R.: Diagnosis of diabetes mellitus using PCA and genetically optimized neural network. In: 2017 International Conference on Computing, Communication and Automation (ICCCA), pp. 334–338. IEEE, May 2017
Mangathayaru, N., Mathura Bai, B., Srikanth, P.: Clustering and classification of effective diabetes diagnosis: computational intelligence techniques using PCA with kNN. In: Satapathy, S.C., Joshi, A. (eds.) ICTIS 2017. SIST, vol. 83, pp. 426–440. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-63673-3_52
Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65(6), 386 (1958)
Minsky, M., Papert, S.A., Bottou, L.: Perceptrons: An Introduction to Computational Geometry. MIT press, Cambridge (2017)
Widrow, B., Hoff, M.E.: Adaptive switching circuits, No. TR-1553–1. Stanford Univ CA Stanford Electronics Labs (1960)
Werbos, P.J.: The Roots of Backpropagation: from Ordered Derivatives to Neural Networks and Political Forecasting, vol. 1. Wiley, Hoboken (1994)
Rumelhalt, D.E.: Learning internal representations by error propagation. Parallel Distrib. process. 1, 318–362 (1986)
Le Cun, Y.: Learning process in an asymmetric threshold network. In: Bienenstock, E., Soulié, F.F., Weisbuch, G. (eds.) Disordered Systems and Biological Organization. NATO ASI Series (Series F: Computer and Systems Sciences), vol. 20, pp. 233–240. Springer, Heidelberg (1986). https://doi.org/10.1007/978-3-642-82657-3_24
Tetko, I.V., Livingstone, D.J., Luik, A.I.: Neural network studies. 1. comparison of overfitting and overtraining. J. Chem. Inf. Comput. Sci. 35(5), 826–833 (1995)
Pima, A.F., Asuncion, A.: Pima Indians Diabetes Data Set. UCI Machine Learning Repository, University of California, Irvine, School of Information and Computer Sciences (2010). http://archive.ics.uci.edu/ml
Balakrishnan, S., Narayanaswamy, R.: Feature selection using fcbf in type ii diabetes databases. Int. J. Comput. Internet Manag. 17(1), 50–58 (2009)
Karegowda, A.G., Manjunath, A.S., Jayaram, M.A.: Comparative study of attribute selection using gain ratio and correlation based feature selection. Int. J. Inf. Technol. Knowl. Manag. 2(2), 271–277 (2010)
Kabir, M.M., Islam, M.M., Murase, K.: A new wrapper feature selection approach using neural network. Neurocomputing 73(16–18), 3273–3283 (2010)
Huang, C.L., Wang, C.J.: A GA-based feature selection and parameters optimization for support vector machines. Expert Syst. Appl. 31(2), 231–240 (2006)
Wang, Y., Li, L., Ni, J., Huang, S.: Feature selection using tabu search with long-term memories and probabilistic neural networks. Pattern Recogn. Lett. 30(7), 661–670 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Haritha, R., Sureshbabu, D., Sammulal, P. (2019). Diabetes Detection Using Principal Component Analysis and Neural Networks. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1036. Springer, Singapore. https://doi.org/10.1007/978-981-13-9184-2_24
Download citation
DOI: https://doi.org/10.1007/978-981-13-9184-2_24
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-9183-5
Online ISBN: 978-981-13-9184-2
eBook Packages: Computer ScienceComputer Science (R0)