A novel approach for prediction of vitamin D status using support vector regression

Guo, Shuyu, Lucas, Robyn M., Ponsonby, Anne-Louise, The Ausimmune Investigator Group, Coulthard, Alan and Pender, Michael P. (2013) A novel approach for prediction of vitamin D status using support vector regression. PLoS ONE, 8 11: e79970.1-e79970.9. doi:10.1371/journal.pone.0079970

Author Guo, Shuyu
Lucas, Robyn M.
Ponsonby, Anne-Louise
The Ausimmune Investigator Group
Coulthard, Alan
Pender, Michael P.
Total Author Count Override 4
Title A novel approach for prediction of vitamin D status using support vector regression
Journal name PLoS ONE   Check publisher's open access policy
ISSN 1932-6203
Publication date 2013-11-26
Year available 2013
Sub-type Article (original research)
DOI 10.1371/journal.pone.0079970
Open Access Status DOI
Volume 8
Issue 11
Start page e79970.1
End page e79970.9
Total pages 9
Place of publication San Francisco, United States
Publisher Public Library of Science
Collection year 2014
Formatted abstract
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.

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.

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).

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.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
School of Medicine Publications
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