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Machine learning for predictive modelling based on small data in biomedical engineering

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Shaikhina, Torgyn, Lowe, David Philip, Daga, Sunil, Briggs, David, Higgins, Rob and Khovanova, N. A. (2015) Machine learning for predictive modelling based on small data in biomedical engineering. IFAC-PapersOnLine, 48 (20). pp. 469-474. doi:10.1016/j.ifacol.2015.10.185

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Official URL: http://dx.doi.org/10.1016/j.ifacol.2015.10.185

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Abstract

Experimental datasets in bioengineering are commonly limited in size, thus rendering Machine Learning (ML) impractical for predictive modelling. Novel techniques of multiple runs for model development and surrogate data analysis for model validation are suggested for prediction of biomedical outcomes based on small datasets for classification and regression tasks. The proposed framework was applied to designing a Neural Network model for osteoarthritic bone fracture risk stratification, and a Decision Tree model for prediction of antibody-mediated kidney transplant rejection. Despite the small datasets (35 bone specimens and 80 kidney transplants), the two models achieved high accuracy of 98.3% and 85%, respectively.

Item Type: Journal Article
Divisions: Faculty of Science, Engineering and Medicine > Engineering > Engineering
Journal or Publication Title: IFAC-PapersOnLine
Publisher: Elsevier
ISSN: 2405-8963
Official Date: 10 November 2015
Dates:
DateEvent
10 November 2015Accepted
1 June 2015Submitted
Volume: 48
Number: 20
Page Range: pp. 469-474
DOI: 10.1016/j.ifacol.2015.10.185
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access

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