- BEng (Electrical and Electrical and Electronics Engineering), Enugu State University of Science and Technology, 2004
- MSc (Computing and Information System), Liverpool John Moores University, 2011
- PhD (Artificial Intelligence), Liverpool John Moores University, 2017
Machine learning, Health Informatics, Text mining, Natural language processing
Dr. Nonso A. Nnamoko
Department of Computer Science
Edge Hill University
Nonso Nnamoko is a post-doctoral researcher in the Horizon 2020 project CROSSMINER. His research focuses on machine learning, text mining and natural language processing. He has also worked on subjects like disease classification, ensemble learning, feature selection, outlier detection, mitigating imbalanced dataset, among others.
Nonso holds a PhD in Artificial Intelligence and a MSc in Computing and Information System from Liverpool John Moores University (LJMU), UK. He also holds a B.Eng. in Electrical and Electronics Engineering from Enugu State University of Science and Technology, Nigeria.
Before progressing to his current role, Nonso worked briefly as an Associate Lecturer in Computing within Edge Hill University. His previous roles elsewhere include Lecturer in Software Engineering at Accrington and Rossendale College, UK; Support Tutor at LJMU, UK; and Research Assistant at the Centre for Health and Social Care Informatics, LJMU, UK.
N. Nnamoko, A. Hussein, D. England. (2018). “Predicting Diabetes Onset: an Ensemble Supervised Learning Approach”; Proceedings of the IEEE International Joint Conference on Neural Networks, July 2018.
N. Nnamoko, F. Arshad, L. Hammond, S. McPartland and P. Patterson (2015) “Telehealth in Primary Healthcare: Analysis of Liverpool NHS Experience”; Elsevier Edited Book – Applied Computing in Health and Medicine, pp. 269 – 286. DOI: 10.1016/B978-0-12-803468-2.00013-8
N. Nnamoko, F. Arshad, D. England, J. Vora and J. Norman (2015) “Fuzzy Inference Model for Diabetes Management: a tool for regimen alterations”; Journal of Computer Sciences and Applications, 3 (3A) pp. 40 – 45. DOI: 10.12691/jcsa-3-3A-5
F.Arshad, N. Nnamoko, J. Wilson, R. Bibhas and M. Taylor (2015) “Improving Healthcare System Usability without real users: a semi-parallel design approach”; International Journal of Healthcare Information Systems and Informatics, 10 (1) pp. 67 – 81. DOI: 10.4018/IJHISI.2015010104
N. Nnamoko, F. Arshad, D. England, J. Vora and J. Norman. (2014) “Meta-classification Model for Diabetes onset forecast: a proof of concept”; Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine, November 2014.
N. Nnamoko, F. Arshad, D. England, J. Vora and J. Norman. (2014) “Evaluation of Filter and Wrapper Methods for Feature Selection in Supervised Machine Learning”, 15th Annual Postgraduate Symposium on the Convergence of Telecommunications, Networking and Broadcasting, June 2014
Nonso Nnamoko (2014) “Social Media: an informal data source for healthcare intervention”; AISB Quarterly Magazine, 138: 20 – 22.
J. Wilson, F. Arshad, N. Nnamoko, A. Whiteman, J. Ring and R. Bibhas (2013) “Patient Reported Outcome Measures PROMs 2.0: an On-Line System Empowering Patient Choice”; Journal of the American Medical Informatics Association, 21 pp. 725-729. DOI:10.1136/amiajnl-2012-001183
N. Nnamoko, F. Arshad, D. England, and J. Vora. (2013) “Fuzzy Expert System for Type 2 Diabetes Mellitus (T2DM) Management using Dual Inference Mechanism,” Proc. AAAI Spring Symposium on Data-driven wellness: From Self tracking to Behaviour modification, 2013
|Abstracts and Posters:
N. Nnamoko, F. Arshad, D. England, J. Vora and J. Norman (2013) “Intelligent Self-care System for Diabetes Support &Management”; Journal of Diabetes Science and Technology, March 2013.
Nonso Nnamoko, Farath Arshad, David England, Professor Jiten Vora (2015) “Evaluation of a Fuzzy Inference Model for continuous regimen alterations in Type 2 Diabetes”, Diabetes UK Professional Conference 2015
N. A. Nnamoko, 2018. Ensemble-based supervised learning for predicting diabetes onset. PhD Thesis, Liverpool John Moores University. Link