Prediction of Vitamin D Deficiency in Older Adults: The Role of Machine Learning Models

Context: Conventional prediction models for vitamin D deficiency have limited accuracy. Background: Using cross-sectional data, we developed models based on machine learning (ML) and compared their performance with those based on a conventional approach. Methods: Participants were 5106 community-res...

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Bibliographic Details
Published in:The journal of clinical endocrinology and metabolism 2022-10, Vol.107 (10), p.2737-2747
Main Authors: Sluyter, John D, Raita, Yoshihiko, Hasegawa, Kohei, Reid, Ian R, Scragg, Robert, Camargo, Carlos A
Format: Article
Language:English
Subjects:
Aged
Alfacalcidol
Calcifediol
Comparative analysis
Machine learning
Neural networks
Vitamin D
Vitamin D deficiency
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Summary:Context: Conventional prediction models for vitamin D deficiency have limited accuracy. Background: Using cross-sectional data, we developed models based on machine learning (ML) and compared their performance with those based on a conventional approach. Methods: Participants were 5106 community-resident adults (50-84 years; 58% male). In the randomly sampled training set (65%), we constructed 5 ML models: lasso regression, elastic net regression, random forest, gradient boosted decision tree, and dense neural network. The reference model was a logistic regression model. Outcomes were deseasonalized serum 25-hydroxyvitamin D (25(OH)D)
ISSN:0021-972X
1945-7197
DOI:10.1210/clinem/dgac432