Int. J. Artificial Intelligence and Soft Computing, Vol. 5, No. 1, 2015 23 Copyright © 2015 Inderscience Enterprises Ltd. A classifier fusion strategy to improve the early detection of neurodegenerative diseases Shamaila Iram*, Paul Fergus, Dhiya Al-Jumeily, Abir Hussain and Martin Randles Applied Computing Research Group, School of Computing and Mathematical Sciences, Liverpool John Moores University, James Parsons Building, Byrom Street, L3 3AF, Liverpool, England, UK Email: S.Iram@2009.ljmu.ac.uk Email: P.Fergus@ljmu.ac.uk Email: D.Aljumeily@ljmu.ac.uk Email: A.Hussain@ljmu.ac.uk Email: M.J.Randles@ljmu.ac.uk *Corresponding author Abstract: People in developed countries are living longer, and this has resulted in the prevalence of age-related diseases like Alzheimer’s and dementia. Many believe that the early detection of neurodegenerative diseases will provide a much more sustainable framework for dealing with age-related diseases in the future. This paper considers this idea and proposes a new classifier fusion strategy that combines classification algorithms and rules (voting, product, mean, median, maximum and minimum) to measure specific behaviours in people suffering with neurodegenerative diseases. More specifically, the fusion strategy analyses the stride-to-stride intervals in gait and its correlation with neurological functions. This approach is compared with base level classifiers (a single classification algorithm) using a set of feature vectors associated with gait patterns obtained from neurodegenerative patients and healthy people. The results show that the fusion strategy improves classification. Our experiments successfully show that a fusion strategy generates better results and classifies subjects more accurately than base level classifiers. Keywords: combining classifiers; pattern recognition; behaviour classification; machine learning; Huntington’s disease; Parkinson’s disease amyotrophic lateral sclerosis; ALS; movement signals; artificial intelligence. Reference to this paper should be made as follows: Iram, S., Fergus, P., Al-Jumeily, D., Hussain, A. and Randles, M. (2015) ‘A classifier fusion strategy to improve the early detection of neurodegenerative diseases’, Int. J. Artificial Intelligence and Soft Computing, Vol. 5, No. 1, pp.23–44. Biographical notes: Shamaila Iram is currently a PhD student of Computer Science at Liverpool John Moores University since 2010. Her area of research is artificial intelligence, bioinformatics, machine learning, and signal processing. In 2012, she has successfully completed a research project on the early detection of Alzheimer’s disease with EEG signals during her internship at ESPCI ParisTech SIGMA Labortary in France. In 2009, she received her