Machine Learning Prediction of Susceptibility to Visceral Fat Associated Diseases
- Submitting institution
-
The University of Westminster
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- qz9v0
- Type
- D - Journal article
- DOI
-
10.1007/s12553-020-00446-1
- Title of journal
- Health and Technology
- Article number
- -
- First page
- 925
- Volume
- 10
- Issue
- -
- ISSN
- 2190-7196
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2020
- 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
- No
- Number of additional authors
-
6
- Research group(s)
-
-
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Research showed strong association of visceral fat (VF), central obesity proxy, with developing serious health complications. VF wraps around abdominal organs deep inside the body. It cannot always be felt or seen and can only be measured with expensive MRI scans.
Our paper is the result of our collaboration with the UK Biobank. It is significant because it produced a highly accurate Artificial Intelligence entity (a backend engine) to web applications to predict VF amounts without undergoing costly scans to speed up intervention. Our work attracted external collaboration with industry for further development to predict VF at individual organ level.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
