Abstract
We are in a machine learning age where several predictive applications that are life dependent are made by machines and robotic devices that relies on ensemble decision making algorithms. These have attracted many researchers and led to the development of an algorithm that is based on the integration of EKF, RBF networks and AdaBoost as an ensemble model to improve prediction accuracy. Firstly, EKF is used to optimize the slow training speed and improve the efficiency of the RBF network training parameters. Secondly, AdaBoost is applied to generate and combine RBFN-EKF weak predictors to form a strong predictor. Breast cancer survivability and diabetes diagnostic datasets used were obtained from the UCI repository. Results are presented on the proposed model as applied to Breast cancer survivability and Diabetes diagnostic predictive problems. The model outputs an accuracy of 96% when EKF-RBFN is applied as a base classifier compare to 94% when Decision Stump is applied and AdaBoost as an ensemble technique in both examples. The output accuracy of ensemble AdaBoostM1-Random Forest and standalone Random Forest models is 97% in both cases. The study has gone some way towards enhancing our knowledge and improving the prediction accuracy through the amalgamation of EKF, RBFN and AdaBoost algorithms as an ensemble model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abuhasel, K., Iliyasu, A., Fatichah, C.: A combined AdaBoost and NEWFM technique for medical data classification. Inf. Sci. Appl. 339, 801–809 (2015)
Adegoke, V.F.: Research report. London South Bank University, Computer Science informatics, School of Engineering, London, UK (2018)
Adegoke, V.F., Chen, D., Barikzai, S., Banissi, E.: Predictive ensemble modelling: experimental comparison of boosting implementation methods. In: European Modelling Symposium (EMS). IEEE, Manchester (2017). https://doi.org/10.1109/EMS.2017.13
Adegoke, V., Chen, D., Banissi, E.: Prediction of breast cancer survivability using ensemble algorithms. In: International Conference on Smart Systems and Technologies (SST). IEEE, Osijek (2017)
Alghamdi, M., Al-Mallah, M., Keteyian, S., Brawner, C., Ehrman, J., Sakr, S.: Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: the Henry Ford ExercIse Testing (FIT) project. PLoS One 12(7) (2017). https://doi.org/10.1371/journal.pone.0179805
Anil, K., Duin, R., Mao, J.: Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 4–37 (2000). https://doi.org/10.1109/34.824819
Apreutesei, N.A., Tircoveanu, F., Cantemir, A., Bogdanici, C., Lisa, C., Curteanu, S., Chiseliţă, D.: Predictions of ocular changes caused by diabetes in glaucoma patients. Comput. Methods Programs Biomed. 154, 183–190 (2018)
Barakat, N., Bradley, A.P., Barakat, M.N.: Intelligible support vector machines for diagnosis of diabetes mellitus. Trans. Inf. Technol. Biomed. 14(4), 1114–1120 (2010)
BBC UK: Labour’s Tom Watson ‘reversed’ type-2 diabetes through diet and exercise. British Broadcasting Corporation, London (2018). http://www.bbc.co.uk/news/uk-politics-45495384. Accessed 12 Sept 2018
BHF: CVD Statistics – BHF UK Factsheet. BHF (British Heart Foundation) (2018). https://www.bhf.org.uk/-/media/files/research/heart-statistics/bhf-cvd-statistics---uk-factsheet.pdf. Accessed 23 Aug 2018
Chernodub, A.: Training neural networks for classification using the extended Kalman filter: a comparative study. Opt. Mem. Neural Netw. 23(2), 96–103 (2014)
Csank, J.T., Connolly, J.W.: Model-based engine control architecture with an extended Kalman filter. The American Institute of Aeronautics and Astronautics, San Diego, California (2016). NASA STI Program. https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20160002248.pdf. Accessed 06 Feb 2018
Fan, M., Zheng, B., Li, L.: A novel Multi-Agent Ada-Boost algorithm for predicting protein structural class with the information of protein secondary structure. J. Bioinform. Comput. Biol. 13(5) (2015). https://doi.org/10.1142/S0219720015500225
Formenti, S., Arslan, A., Love, S.: Global breast cancer: the lessons to bring home. Int. J. Breast Cancer (2012)
Freund, Y., Schapire, R.: A decision-theoretic generalization of on-line learning and application to boosting. J. Comput. Syst. Sci. 55, 119–139 (1997). http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.32.8918
Haykin, S.: Adaptive Filter Theory, 3rd edn. Prentice-Hall, Upper Saddle River (1996)
ITV: Surge in heart attacks and strokes predicted as diabetes epidemic takes its toll. ITV Report, London (2018). https://www.itv.com/news/2018-08-23/surge-in-heart-attacks-and-strokes-predicted-as-diabetes-epidemic-takes-its-toll/. Accessed 06 Aug 2018
Karimi, P., Jazayeri-Rad, H.: Comparing the fault diagnosis performances of single neural networks and two ensemble neural networks based on the boosting methods. J. Autom. Control. 2(1), 21–32 (2014)
Kwon, S., Lee, S.: Recent advances in microwave imaging for breast cancer detection. Int. J. Biomed. Imaging (2016). https://doi.org/10.1155/2016/5054912
Lee, Y., Han, D., Ko, H.: Reinforced AdaBoost learning for object detection with local pattern representations. Sci. World J. 2013, 14 (2013). https://doi.org/10.1155/2013/153465
Lima, D., Sanches, R., Pedrino, E.: Neural network training using unscented and extended Kalman filter. Robot. Autom. Eng. J. 1(4) (2017)
McGinley, B., O’Halloran, M., Conceicao, R., Morgan, F., Glavin, M., Jones, E.: Spiking neural networks for breast cancer classification in a dielectrically heterogeneous breast. Prog. Electromagn. Res. C 17, 74–94 (2010). https://doi.org/10.2528/PIERC10100202
Merwe, R., Nelson, A., Wan, E.: An introduction to Kalman filtering. OGI School of Science & Engineering Lecture (2004)
Moreno, V.M., Pigazo, A.: Kalman Filter: Recent Advances and Applications. I-Tech Education and Publishing KG, Vienna (2009)
Nabney, I.: NETLAB Algorithms for Pattern Recognition (Ed. by M. Singh). Springer, London (2002)
Pak, F., Kanan, H., Alikhassi, A.: Breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and super resolution. Comput. Methods Programs Biomed. 122, 89–107 (2015)
Ribeiro, M.: Kalman and extended Kalman filters: concept, derivation and properties. CiteSeer (2004)
Sapate, S., Talbar, S.: An overview of pectoral muscle extraction algorithms applied to digital mammograms. In: Studies in Computational Intelligence (2016). https://doi.org/10.1007/978-3-319-33793-7_2
Schapire, R., Freund, Y.: Boosting: Foundations and Algorithms, 2nd edn. MIT Press, Cambridge (2014)
Simon, D.: Training radial basis neural networks with the extended Kalman filter. Neurocomputing 48, 455–457 (2002)
American Cancer Society: Breast Cancer Facts & Figures 2015-2016 (Ed. by DeSantis, R. Siegel, A. Jemal) (2015). American Cancer Society https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/breast-cancer-facts-and-figures/breast-cancer-facts-and-figures-2015-2016.pdf. Accessed 05 May 2017
Cancer Research UK: Cancer Research UK (2018). http://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/breast-cancer. Accessed 20 Aug 2018
Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)
Wan, E., Van Der Merwe, R.: The unscented Kalman filter for nonlinear estimation. In: Symposium on Adaptive Systems for Signal Processing Communications and Control, pp. 153–158 (2000). https://doi.org/10.1109/ASSPCC.2000.882463
Weedon-Fekjær, H., Romundstad, P., Vatten, L.: Modern mammography screening and breast cancer mortality: population study. BMJ 348, g3701 (2014). https://doi.org/10.1136/bmj.g3701
WHO: Global report on diabetes - World Health Organization. WHO Library Cataloguing-in-Publication Data, Geneva (2016). http://apps.who.int/iris/bitstream/handle/10665/204871/9789241565257_eng.pdf;jsessionid=DFE5616C3480A8F293D9970CC0FA4EF1?sequence=1
Xie, W., Li, Y., Ma, Y.: Breast mass classification in digital mammography based on extreme learning machine. Neurocomputing 73(3), 930–941 (2015). https://doi.org/10.1016/j.neucom.2015.08.048
Yang, C.-H., Lin, Y.-U., Chuang, L.-Y., Chang, H.-W.: Evaluation of breast cancer susceptibility using improved genetic algorithms to generate genotype SNP barcodes. IEEE/ACM Trans. Comput. Biol. Bioinf. 10(2), 361–371 (2013). https://doi.org/10.1109/TCBB.2013.27
Zheng, T., Xie, W., Xu, L., He, X., Zhang, Y., You, M., Chen, Y.: A machine learning-based framework to identify type 2 diabetes through electronic health records. Int. J. Med. Inform. 97, 120–127 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Adegoke, V., Chen, D., Banissi, E., Barsikzai, S. (2020). Enhancing Ensemble Prediction Accuracy of Breast Cancer Survivability and Diabetes Diagnostic Using Optimized EKF-RBFN Trained Prototypes. In: Madureira, A., Abraham, A., Gandhi, N., Silva, C., Antunes, M. (eds) Proceedings of the Tenth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2018). SoCPaR 2018. Advances in Intelligent Systems and Computing, vol 942. Springer, Cham. https://doi.org/10.1007/978-3-030-17065-3_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-17065-3_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-17064-6
Online ISBN: 978-3-030-17065-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)