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.
Authors | Adegoke, V, Chen, D and Banissi, E |
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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. |
Year | 2019 |
Journal | International Journal of Computer Information Systems and Industrial Management |
Journal citation | 11, pp. 82-100 |
Publisher | Machine Intelligence Research Labs |
ISSN | 2150-7988 |
Web address (URL) | http://www.mirlabs.org/ijcisim/regular_papers_2019/IJCISIM_9.pdf |
Publication dates | |
25 Apr 2019 | |
Publication process dates | |
Deposited | 13 May 2019 |
Accepted | 28 Mar 2019 |
Accepted author manuscript | License File Access Level Open |
Permalink -
https://openresearch.lsbu.ac.uk/item/8671x
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Accepted author manuscript
Improving Prediction Accuracy of Breast Cancer Survivability and Diabetes Diagnosis via RBF Networks trained with EKF models.pdf | ||
License: CC BY 4.0 | ||
File access level: Open |
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