Please use this identifier to cite or link to this item: 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.
Ponsonby, A.
Chapman, C.
Coulthard, A.
Dear, K.
Dwyer, T.
Kilpatrick, T.
McMichael, T.
Pender, M.
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
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