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

Guo, Shuyu, Lucas, Robyn M., Valery, Patricia C. and Ausimmune Investigator Group (2013). A novel approach for prediction of vitamin D status using support vector regression. PLoS One,8(11):e79970-1-e79970-9.

Document type: Journal Article
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IRMA ID 75039815xPUB520
Title A novel approach for prediction of vitamin D status using support vector regression
Author Guo, Shuyu
Lucas, Robyn M.
Valery, Patricia C.
Ausimmune Investigator Group
Journal Name PLoS One
Publication Date 2013
Volume Number 8
Issue Number 11
ISSN 1932-6203   (check CDU catalogue open catalogue search in new window)
Scopus ID 2-s2.0-84896728717
Start Page e79970-1
End Page e79970-9
Total Pages 9
Place of Publication United States
Publisher Public Library of Science
HERDC Category C1 - Journal Article (DIISR)
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.
DOI http://dx.doi.org/10.1371/journal.pone.0079970   (check subscription with CDU E-Gateway service for CDU Staff and Students  check subscription with CDU E-Gateway in new window)


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