A classifier fusion strategy to improve the early detection of neurodegenerative diseases
- Submitting institution
-
The University of Huddersfield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1
- Type
- D - Journal article
- DOI
-
10.1504/IJAISC.2015.067525
- Title of journal
- International Journal of Artificial Intelligence and Soft Computing
- Article number
- -
- First page
- 23
- Volume
- 5
- Issue
- 1
- ISSN
- 1755-4950
- Open access status
- Out of scope for open access requirements
- Month of publication
- February
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
4
- Research group(s)
-
-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "The paper introduces a novel classification algorithm that is designed by the fusion of well-known, pre-evaluated classifiers to detect Dementia at different stages. The work is a result of collaborating with the Machine Learning Lab SIGMA at ESPCI Paris (https://en.wikipedia.org/wiki/ESPCI_Paris) leading to co-authorship of other influential work https://www.hindawi.com/journals/tswj/2015/931387/. The subsequent method is described as very promising for the early diagnosis of AD - see p25 of
https://www.researchgate.net/publication303932554_On_the_early_diagnosis_of_Alzheimer%27s_Disease_from_multimodal_signals_A_survey
This publication forms a key part of Iram's research on developing novel methodological frameworks to diagnose Alzheimer’s at an earlier stage - more details in non-submitted papers: https://link.springer.com/chapter/10.1007/978-3-319-09330-7_4,
https://www.sciencedirect.com/science/article/pii/B9780128034682000011, and
https://ieeexplore.ieee.org/abstract/document/6866646"
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -