Department of Orthopedics, Chaohu Hospital of Anhui Medical University, Hefei, China.
Department of Infection, Chaohu Hospital of Anhui Medical University, Hefei, China.
Front Endocrinol (Lausanne). 2024 Feb 16;15:1327058. doi: 10.3389/fendo.2024.1327058. eCollection 2024.
Vitamin D deficiency is strongly associated with the development of several diseases. In the current context of a global pandemic of vitamin D deficiency, it is critical to identify people at high risk of vitamin D deficiency. There are no prediction tools for predicting the risk of vitamin D deficiency in the general community population, and this study aims to use machine learning to predict the risk of vitamin D deficiency using data that can be obtained through simple interviews in the community.
The National Health and Nutrition Examination Survey 2001-2018 dataset is used for the analysis which is randomly divided into training and validation sets in the ratio of 70:30. GBM, LR, NNet, RF, SVM, XGBoost methods are used to construct the models and their performance is evaluated. The best performed model was interpreted using the SHAP value and further development of the online web calculator.
There were 62,919 participants enrolled in the study, and all participants included in the study were 2 years old and above, of which 20,204 (32.1%) participants had vitamin D deficiency. The models constructed by each method were evaluated using AUC as the primary evaluation statistic and ACC, PPV, NPV, SEN, SPE, F1 score, MCC, Kappa, and Brier score as secondary evaluation statistics. Finally, the XGBoost-based model has the best and near-perfect performance. The summary plot of SHAP values shows that the top three important features for this model are race, age, and BMI. An online web calculator based on this model can easily and quickly predict the risk of vitamin D deficiency.
In this study, the XGBoost-based prediction tool performs flawlessly and is highly accurate in predicting the risk of vitamin D deficiency in community populations.
维生素 D 缺乏与多种疾病的发生密切相关。在当前全球维生素 D 缺乏症大流行的背景下，确定维生素 D 缺乏高危人群至关重要。目前尚无预测一般社区人群维生素 D 缺乏风险的预测工具，本研究旨在使用机器学习，利用社区中通过简单访谈即可获得的数据来预测维生素 D 缺乏的风险。
使用 2001-2018 年全国健康与营养调查（NHANES）数据集进行分析，将数据随机分为训练集和验证集，比例为 70:30。使用 GBM、LR、NNet、RF、SVM、XGBoost 方法构建模型，并评估其性能。使用 SHAP 值对表现最佳的模型进行解释，并进一步开发在线网络计算器。
本研究共纳入 62919 名参与者，所有纳入研究的参与者年龄均在 2 岁及以上，其中 20204 名（32.1%）参与者存在维生素 D 缺乏。使用 AUC 作为主要评估指标，ACC、PPV、NPV、SEN、SPE、F1 评分、MCC、Kappa 和 Brier 评分作为次要评估指标，对每种方法构建的模型进行评估。最终，基于 XGBoost 的模型具有最佳且接近完美的性能。SHAP 值的汇总图显示，对于该模型，最重要的三个特征是种族、年龄和 BMI。基于该模型的在线网络计算器可以方便、快速地预测维生素 D 缺乏的风险。
在本研究中，基于 XGBoost 的预测工具表现完美，在预测社区人群维生素 D 缺乏风险方面具有高度准确性。
