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

<h4>Background</h4>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...

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Autores principales:	Shuyu Guo, Robyn M Lucas, Anne-Louise Ponsonby, Ausimmune Investigator Group
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e4a54b73d54641498cca81f24762886d
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spelling	oai:doaj.org-article:e4a54b73d54641498cca81f24762886d2021-11-18T08:44:44ZA novel approach for prediction of vitamin d status using support vector regression.1932-620310.1371/journal.pone.0079970https://doaj.org/article/e4a54b73d54641498cca81f24762886d2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24302994/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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).<h4>Conclusion</h4>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.Shuyu GuoRobyn M LucasAnne-Louise PonsonbyAusimmune Investigator GroupPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 11, p e79970 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shuyu Guo Robyn M Lucas Anne-Louise Ponsonby Ausimmune Investigator Group A novel approach for prediction of vitamin d status using support vector regression.
description	<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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).<h4>Conclusion</h4>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.
format	article
author	Shuyu Guo Robyn M Lucas Anne-Louise Ponsonby Ausimmune Investigator Group
author_facet	Shuyu Guo Robyn M Lucas Anne-Louise Ponsonby Ausimmune Investigator Group
author_sort	Shuyu Guo
title	A novel approach for prediction of vitamin d status using support vector regression.
title_short	A novel approach for prediction of vitamin d status using support vector regression.
title_full	A novel approach for prediction of vitamin d status using support vector regression.
title_fullStr	A novel approach for prediction of vitamin d status using support vector regression.
title_full_unstemmed	A novel approach for prediction of vitamin d status using support vector regression.
title_sort	novel approach for prediction of vitamin d status using support vector regression.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/e4a54b73d54641498cca81f24762886d
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A novel approach for prediction of vitamin d status using support vector regression.

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