Research Repository

Applied Machine Learning for the Prediction of Growth of Abdominal Aortic Aneurysm in Humans

Lee, R and Jarchi, D and Perera, R and Jones, A and Cassimjee, I and Handa, A and Clifton, DA and Bellamkonda, K and Woodgate, F and Killough, N and Maistry, N and Chandrashekar, A and Darby, CR and Halliday, A and Hands, LJ and Lintott, P and Magee, TR and Northeast, A and Perkins, J and Sideso, E (2018) 'Applied Machine Learning for the Prediction of Growth of Abdominal Aortic Aneurysm in Humans.' EJVES Short Reports, 39. pp. 24-28. ISSN 2405-6553

Applied Machine Learning for the Prediction of Growth of Abdominal Aortic Aneurysm in Humans.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (469kB) | Preview


Objective: Accurate prediction of abdominal aortic aneurysm (AAA) growth in an individual can allow personalised stratification of surveillance intervals and better inform the timing for surgery. The authors recently described the novel significant association between flow mediated dilatation (FMD) and future AAA growth. The feasibility of predicting future AAA growth was explored in individual patients using a set of benchmark machine learning techniques. Methods: The Oxford Abdominal Aortic Aneurysm Study (OxAAA) prospectively recruited AAA patients undergoing the routine NHS management pathway. In addition to the AAA diameter, FMD was systemically measured in these patients. A benchmark machine learning technique (non-linear Kernel support vector regression) was applied to predict future AAA growth in individual patients, using their baseline FMD and AAA diameter as input variables. Results: Prospective growth data were recorded at 12 months (360 ± 49 days) in 94 patients. Of these, growth data were further recorded at 24 months (718 ± 81 days) in 79 patients. The average growth in AAA diameter was 3.4% at 12 months, and 2.8% per year at 24 months. The algorithm predicted the individual's AAA diameter to within 2 mm error in 85% and 71% of patients at 12 and 24 months. Conclusions: The data highlight the utility of FMD as a biomarker for AAA and the value of machine learning techniques for AAA research in the new era of precision medicine.

Item Type: Article
Uncontrolled Keywords: Abdominal aortic aneurysm, Aneurysm progression, Machine learning, Biomarker, Flow mediated dilatation
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > R Medicine (General)
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 11 Jun 2019 09:41
Last Modified: 23 Sep 2022 19:32

Actions (login required)

View Item View Item