Machine Learning Approaches for Fracture Risk Assessment: A Comparative Analysis of Genomic and Phenotypic Data in 5130 Older Men.
Machine Learning Approaches for Fracture Risk Assessment: A Comparative Analysis of Genomic and Phenotypic Data in 5130 Older Men.
复制标题
用于骨折风险评估的机器学习方法：5130 名老年男性基因组和表型数据的比较分析。
DOI：
10.1007/s00223-020-00734-y
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发表时间：
2020
影响因子：
4.2
通讯作者：
Han,MiraV
中科院分区：
文献类型：
--
作者：
Wu,Qing;Nasoz,Fatma;Jung,Jongyun;Bhattarai,Bibek;Han,MiraV
该研究的目的是通过使用机器学习方法和基因组数据开发裂缝预测模型，并确定裂缝预测的最佳建模方法。分析了男性骨质疏松性骨折队列研究（n = 5130）的基因组数据。经过全面的基因型插补后，根据每位参与者的1103个相关单核苷酸多态性计算遗传风险评分（GRS）。将数据标准化并分成训练集（80%）和验证集（20%）用于分析。随机森林、梯度推进、神经网络和逻辑回归分别用于开发主要骨质疏松性骨折的预测模型，以GRS、骨密度和其他危险因素作为预测因子。在模型训练中，使用合成少数过采样技术来解释低断裂率，并使用十倍交叉验证来优化超参数。在测试中，曲线下面积（AUC）和准确度用于评估模型性能。采用McNemar检验来检查模型之间的准确性差异。结果表明，梯度推进法的预测性能最好，AUC为0.71，准确率为0.88，GRS在模型中排名第7位。随机森林和神经网络的性能也明显优于逻辑回归。这项研究表明，提高老年男性骨折预测可以通过整合基因分析和利用梯度提升方法来实现。这一结果不应外推到妇女或年轻人身上。
The study aims were to develop fracture prediction models by using machine learning approaches and genomic data, as well as to identify the best modeling approach for fracture prediction. The genomic data of Osteoporotic Fractures in Men, cohort Study (n= 5130), were analyzed. After a comprehensive genotype imputation, genetic risk score (GRS) was calculated from 1103 associated Single Nucleotide Polymorphisms for each participant. Data were normalized and split into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and logistic regression were used to develop prediction models for major osteoporotic fractures separately, with GRS, bone density, and other risk factors as predictors. In model training, the synthetic minority oversampling technique was used to account for low fracture rate, and tenfold cross-validation was employed for hyperparameters optimization. In the testing, the area under curve (AUC) and accuracy were used to assess the model performance. The McNemar test was employed to examine the accuracy difference between models. The results showed that the prediction performance of gradient boosting was the best, with AUC of 0.71 and an accuracy of 0.88, and the GRS ranked as the 7th most important variable in the model. The performance of random forest and neural network were also significantly better than that of logistic regression. This study suggested that improving fracture prediction in older men can be achieved by incorporating genetic profiling and by utilizing the gradient boosting approach. This result should not be extrapolated to women or young individuals.