Dr Nonso Alexanda Nnamoko

  • BEng Electrical and Electrical and Electronics Engineering
  • MSc Computing and Information Systems
  • PhD Artificial Intelligence
    • Associate Fellow of HEA
    • Member of BCS
Nonso Nnamoko 
Research Contact Teaching
Machine learning, Health Informatics, Text mining, Natural language processing

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Dr. Nonso A. Nnamoko
Department of Computer Science
Edge Hill University
L39 4QPEmail: nnamokon@edgehill.ac.uk
Office: THF10
  • Software Engineering
  • Databases and Data Modelling


Nonso is a Lecturer in Computer Science at Edge Hill University (EHU). He is research active in the areas of Machine Learning, Big Data analytics, Software Engineering, Natural Language Processing and Computational Linguistics. He has applied this knowledge on subjects like disease classification and diagnosis, text mining, ensemble learning, outlier detection, imbalanced data problems, among others.
Nonso holds a PhD in Artificial Intelligence, MSc in Computing and Information System and BEng (Hons) in Electrical and Electronic Engineering. Before his present role, Nonso held two research roles at EHU – the first as a post-doctoral researcher in a Text Mining project (CROSSMINER) funded under the European Union’s Horizon 2020 Research and Innovation Programme; and another in Computational Linguistics. Other previous roles include Associate Tutor at EHU, Lecturer in Software Engineering at ACROSS, Support Tutor at LJMU and Research Assistant at the Centre for Health and Social Care Informatics, LJMU.

Research Publications

Research Articles:
  • Nnamoko, N., & Korkontzelos, I. (2020). Efficient treatment of outliers and class imbalance for diabetes prediction. Artificial Intelligence in Medicine, 104, 101815. https://doi.org/10.1016/j.artmed.2020.101815
  • Nnamoko, N., Cabrera-Diego, L. A., Campbell, D., & Korkontzelos, Y. (2019). Bug Severity Prediction Using a Hierarchical One-vs.-Remainder Approach. In E. Métais, F. Meziane, V. Sunil, V. Sugumaran, & M. Saraee (Eds.), International Conference on Applications of Natural Language to Information Systems, NLDB (pp. 247–260). https://doi.org/10.1007/978-3-030-23281-8_20
  • 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
  • Theses:
  • N. A. Nnamoko, 2018. Ensemble-based supervised learning for predicting diabetes onset. PhD Thesis, Liverpool John Moores University. Link
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