Enhancing Ensemble Prediction Accuracy of Breast Cancer Survivability and Diabetes Diagnostic Using Optimized EKF-RBFN Trained Prototypes-Reference-Cited by-同舟云学术

Enhancing Ensemble Prediction Accuracy of Breast Cancer Survivability and Diabetes Diagnostic Using Optimized EKF-RBFN Trained Prototypes

Published:2019-04-10 Issue: Volume: Page:51-65
ISSN:2194-5357
Container-title:Proceedings of the Tenth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2018)
language:
Short-container-title:

Author:

Adegoke Vincent,Chen Daqing,Banissi Ebad,Barsikzai Safia

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-17065-3_6

Reference39 articles.

1. Abuhasel, K., Iliyasu, A., Fatichah, C.: A combined AdaBoost and NEWFM technique for medical data classification. Inf. Sci. Appl. 339, 801–809 (2015)

2. Adegoke, V.F.: Research report. London South Bank University, Computer Science informatics, School of Engineering, London, UK (2018)

3. 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

4. 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)

5. 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

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. The Changing Global Landscape With Emerging Technologies and Their Implications for Smart Societies;Handbook of Research on 5G Networks and Advancements in Computing, Electronics, and Electrical Engineering;2021

2. Diagnosing malaria from some symptoms: a machine learning approach and public health implications;Health and Technology;2020-10-25