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https://hdl.handle.net/2440/118450
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Type: | Journal article |
Title: | A novel approach for prediction of vitamin D status using support vector regression |
Author: | Guo, S. Lucas, R.M. Ponsonby, A.L. Chapman, C. Coulthard, A. Dear, K. Dwyer, T. Kilpatrick, T. McMichael, T. Pender, M.P. Taylor, B. Valery, P. Van Der Mei, I. Williams, D. |
Citation: | PLoS One, 2013; 8(11):e79970-1-e79970-9 |
Publisher: | Public Library Science |
Issue Date: | 2013 |
ISSN: | 1932-6203 1932-6203 |
Editor: | de Brevern, A.G. |
Statement of Responsibility: | Shuyu Guo, Robyn M. Lucas, Anne-Louise Ponsonby, the Ausimmune Investigator Group |
Abstract: | Background: Epidemiological evidence suggests that vitamin D deficiency is linked to various chronic diseases. However direct measurement of serum 25-hydroxyvitamin D (25(OH)D) concentration, the accepted biomarker of vitamin D status, may not be feasible in large epidemiological studies. An alternative approach is to estimate vitamin D status using a predictive model based on parameters derived from questionnaire data. In previous studies, models developed using Multiple Linear Regression (MLR) have explained a limited proportion of the variance and predicted values have correlated only modestly with measured values. Here, a new modelling approach, nonlinear radial basis function support vector regression (RBF SVR), was used in prediction of serum 25(OH)D concentration. Predicted scores were compared with those from a MLR model. Methods: Determinants of serum 25(OH)D in Caucasian adults (n = 494) that had been previously identified were modelled using MLR and RBF SVR to develop a 25(OH)D prediction score and then validated in an independent dataset. The correlation between actual and predicted serum 25(OH)D concentrations was analysed with a Pearson correlation coefficient. Results: Better correlation was observed between predicted scores and measured 25(OH)D concentrations using the RBF SVR model in comparison with MLR (Pearson correlation coefficient: 0.74 for RBF SVR; 0.51 for MLR). The RBF SVR model was more accurately able to identify individuals with lower 25(OH)D levels (<75 nmol/L). Conclusion: Using identical determinants, the RBF SVR model provided improved prediction of serum 25(OH)D concentrations and vitamin D deficiency compared with a MLR model, in this dataset. |
Keywords: | Ausimmune Investigator Group Humans Vitamin D Deficiency Vitamin D Linear Models ROC Curve Algorithms Support Vector Machine |
Rights: | © 2013 Guo et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
DOI: | 10.1371/journal.pone.0079970 |
Grant ID: | NHMRC |
Published version: | http://dx.doi.org/10.1371/journal.pone.0079970 |
Appears in Collections: | Aurora harvest 8 Public Health publications |
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hdl_118450.pdf | Published version | 1.15 MB | Adobe PDF | View/Open |
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