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Handling limited datasets with neural networks in medical applications : a small-data approach
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Shaikhina, Torgyn and Khovanova, N. A. (2017) Handling limited datasets with neural networks in medical applications : a small-data approach. Artificial Intelligence in Medicine, 75 . pp. 51-63. doi:10.1016/j.artmed.2016.12.003 ISSN 0933-3657.
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Official URL: http://doi.org/10.1016/j.artmed.2016.12.003
Abstract
Motivation: Single-centre studies in medical domain are often characterised by limited samples due to the complexity and high costs of patient data collection. Machine learning methods for regression modelling of small datasets (less than 10 observations per predictor variable) remain scarce. Our work bridges this gap by developing a novel framework for application of artificial neural networks (NNs) for regression tasks involving small medical datasets.
Methods: In order to address the sporadic fluctuations and validation issues that appear in regression NNs trained on small datasets, the method of multiple runs and surrogate data analysis were proposed in this work. The approach was compared to the state-of-the-art ensemble NNs; the effect of dataset size on NN performance was also investigated.
Results: The proposed framework was applied for the prediction of compressive strength (CS) of femoral trabecular bone in patients suffering from severe osteoarthritis. The NN model was able to estimate the CS of osteoarthritic trabecular bone from its structural and biological properties with a standard error of 0.85 MPa. When evaluated on independent test samples, the NN achieved accuracy of 98.3%, outperforming an ensemble NN model by 11%. We reproduce this result on CS data of another porous solid (concrete) and demonstrate that the proposed framework allows for an NN modelled with as few as 56 samples to generalise on 300 independent test samples with 86.5% accuracy, which is comparable to the performance of an NN developed with 18 times larger dataset (1030 samples).
Conclusion: The significance of this work is two-fold: the practical application allows for non-destructive prediction of bone fracture risk, while the novel methodology extends beyond the task considered in
this study and provides a general framework for application of regression NNs to medical problems characterised by limited dataset sizes.
Item Type: | Journal Article | ||||||
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Subjects: | R Medicine > RC Internal medicine T Technology > TA Engineering (General). Civil engineering (General) |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||
Library of Congress Subject Headings (LCSH): | Fractures -- Risk factors, Bones -- Compressive strength, Regression analysis -- Data processing, Osteoarthritis -- Treatment, Concrete -- Compressive strength | ||||||
Journal or Publication Title: | Artificial Intelligence in Medicine | ||||||
Publisher: | Elsevier BV | ||||||
ISSN: | 0933-3657 | ||||||
Official Date: | 3 January 2017 | ||||||
Dates: |
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Volume: | 75 | ||||||
Page Range: | pp. 51-63 | ||||||
DOI: | 10.1016/j.artmed.2016.12.003 | ||||||
Status: | Peer Reviewed | ||||||
Publication Status: | Published | ||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||
Date of first compliant deposit: | 15 February 2017 | ||||||
Date of first compliant Open Access: | 20 February 2017 | ||||||
Funder: | Engineering and Physical Sciences Research Council (EPSRC) | ||||||
Grant number: | EP/K02504X/1 | ||||||
Open Access Version: |
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