Abstract
Diabetes is one of the most frightful diseases that is creating a terror in peoples mind all over the globe and all of them are putting tremendous efforts to search for various methods to prevent this disease at the budding stage by predicting the symptoms of diabetes. In this paper, our main aim is to predict the onset of diabetes amongst women aged at least 21 years using Two-class Neural Network and tabulate and compare our results with others results. This approach has been tested with the Pima Indians Diabetes Data Set downloaded from the UCI Machine Learning data repository. The performance of our predictive model has been measured and compared in terms of accuracy and recall. Endocrinologists, dietitians, ophthalmologists and podiatrists can use this model to predict how likely a patient is to suffer from diabetes.
This is a preview of subscription content, log in via an institution to check access.
References
Kandhasamy, J.P., Balamurali, S.: Performance analysis of classifier models to predict diabetes mellitus. Procedia Comput. Sci. 47, 45–51 (2015)
Gaber, M.M., Zaslavsky, A., Krishnaswamy, S.: Mining data streams: a review. ACM Sigmod Rec. 34(2), 18–26 (2005)
Palaniappan, S., Awang, R.: Intelligent heart disease prediction system using data mining techniques. In: 2008 IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2008, pp. 108–115. IEEE (2008)
Jia, Z., Zhou, Y., Liu, X., Wang, Y., Zhao, X., Wang, Y., Liang, W., Shouling, W.: Comparison of different anthropometric measures as predictors of diabetes incidence in a Chinese population. Diabetes Res. Clin. Pract. 92(2), 265–271 (2011)
Tapak, L., Mahjub, H., Hamidi, O., Poorolajal, J.: Real-data comparison of data mining methods in prediction of diabetes in Iran. Healthc. Inf. Res. 19(3), 177–185 (2013)
Olaniyi, E.O., Adnan, K.: Onset diabetes diagnosis using artificial neural network. Int. J. Sci. Eng. Res. 5(10) (2014)
Pradhan, M., Sahu, R.K.: Predict the onset of diabetes disease using artificial neural network (ANN). Int. J. Comput. Sci. Emerg. Technol. 2(2), 303–311 (2011)
Jack W Smith, JE Everhart, WC Dickson, WC Knowler, and RS Johannes. Using the adap learning algorithm to forecast the onset of diabetes mellitus. In Proceedings of the Annual Symposium on Computer Application in Medical Care, p. 261. American Medical Informatics Association, 1988
Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349(6245), 255–260 (2015)
Shanker, M.S.: Using neural networks to predict the onset of diabetes mellitus. J. Chem. Inf. Comput. Sci. 36(1), 35–41 (1996)
Kayaer, K.: Medical diagnosis on pima Indian diabetes using general regression neural networks
Temurtas, H., Yumusak, N., Temurtas, F.: A comparative study on diabetes disease diagnosis using neural networks. Expert Syst. Appl. 36(4), 8610–8615 (2009)
UCI Machine Learning Repository pima Indian diabetes data set. https://archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes
Powers, D.M.: Evaluation: from precision, recall and f-measure to ROC, informedness, markedness and correlation (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rakshit, S. et al. (2017). Prediction of Diabetes Type-II Using a Two-Class Neural Network. In: Mandal, J., Dutta, P., Mukhopadhyay, S. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2017. Communications in Computer and Information Science, vol 776. Springer, Singapore. https://doi.org/10.1007/978-981-10-6430-2_6
Download citation
DOI: https://doi.org/10.1007/978-981-10-6430-2_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6429-6
Online ISBN: 978-981-10-6430-2
eBook Packages: Computer ScienceComputer Science (R0)