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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
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: |
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| 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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