Improving Prediction Accuracy of Breast Cancer Survivability and Diabetes Diagnosis via RBF Networks trained with EKF models

Journal article

Adegoke, V, Chen, D and Banissi, E (2019). Improving Prediction Accuracy of Breast Cancer Survivability and Diabetes Diagnosis via RBF Networks trained with EKF models. International Journal of Computer Information Systems and Industrial Management. 11, pp. 82-100.
AuthorsAdegoke, V, Chen, D and Banissi, E
Abstract

The continued reliance on machine learning algorithms and robotic devices in the medical and engineering practices has prompted the need for the accuracy prediction of such devices. It has attracted many researchers in recent years and has led to the development of various ensembles and standalone models to address prediction accuracy issues. This study was carried out to investigate the integration of EKF, RBF networks and AdaBoost as an ensemble model to improve prediction accuracy. In this study we proposed a model termed EKF-RBFN-ADABOOST.

Year2019
JournalInternational Journal of Computer Information Systems and Industrial Management
Journal citation11, pp. 82-100
PublisherMachine Intelligence Research Labs
ISSN2150-7988
Web address (URL)http://www.mirlabs.org/ijcisim/regular_papers_2019/IJCISIM_9.pdf
Publication dates
Print25 Apr 2019
Publication process dates
Deposited13 May 2019
Accepted28 Mar 2019
Accepted author manuscript
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Open
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