Interpretable machine learning to identify important predictors of birth weight: A prospective cohort study.
Interpretable machine learning to identify important predictors of birth weight: A prospective cohort study.
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可解释的机器学习识别出生体重的重要预测因素：一项前瞻性队列研究
DOI：
10.3389/fped.2022.899954
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发表时间：
2022
影响因子：
2.6
通讯作者：
Wang, Hai-Jun
中科院分区：
文献类型：
--
作者：
Liu, Zheng;Han, Na;Su, Tao;Ji, Yuelong;Bao, Heling;Zhou, Shuang;Luo, Shusheng;Wang, Hui;Liu, Jue;Wang, Hai-Jun
背景预测出生体重并确定其危险因素在临床上具有重要意义。本研究旨在使用可解释的机器学习来预测出生体重和身份的重要预测因素。方法本前瞻性队列研究在中国北京通州妇幼保健院进行，招募2018年6月至2019年2月的孕妇。我们使用了24个特征来预测婴儿出生体重，包括胎龄、母亲年龄、胎次、巨大儿分娩史、孕前体重指数（BMI）、身高、父亲BMI、生活方式（饮食、身体活动、吸烟）和生物标志物（空腹血糖和血脂）特征。研究结果为婴儿出生体重。我们使用了8个监督学习模型，包括4个个体[线性回归、ridge回归、lasso回归、支持向量机回归（SVR）]和4个集合估计器（随机森林、AdaBoost、梯度增强树和投票集合回归）来预测出生体重。通过训练集上10倍交叉验证的均方根误差（RMSE）和测试集上预测的均方根误差（RMSE）来衡量模型的准确性。我们使用排列重要性算法来理解模型的预测以及影响它们的因素。结果本研究共纳入4,754对母子。在回归、线性回归和SVR的投票集合中，rmse低于随机森林、AdaBoost和梯度增强树。婴儿出生体重的5个最重要的预测因素是胎龄、胎儿性别、早产、母亲身高和孕前体重指数。在将超声测量的胎儿生长指标加入预测指标后，母亲的身高和孕前BMI仍然是预测结果最重要的预测指标。结论母亲身高和孕前BMI是影响婴儿出生体重的重要因素。可解释的机器学习在预测出生体重方面是一个很有前途的工具。
Background Predicting birth weight and identifying its risk factors are clinically important. This study aims to use interpretable machine learning to predict birth weight and identity important predictors. Methods This prospective cohort study was conducted in Tongzhou Maternal and Child Health Care Hospital of Beijing, China, recruiting pregnant women between June 2018 and February 2019. We used 24 features to predict infant birth weight, including gestational age, mother's age, parity, history of macrosomia delivery, pre-pregnancy body mass index (BMI), height, father's BMI, lifestyle (diet, physical activity, smoking), and biomarker (fasting glucose and lipids) features. Study outcome was birth weight of infant. We used 8 supervised learning models including 4 individual [linear regression, ridge regression, lasso regression, support vector machines regression (SVR)], and 4 ensemble estimators (random forest, AdaBoost, gradient boosted trees, and voting ensemble for regression) to predict birth weight. Model accuracy was measured by root mean squared error (RMSE) of 10-fold cross validation on the training set and RMSE of prediction on the test set. We used permutation importance algorithm to understand the prediction from the models and what affected them. Result This study included 4,754 mother-child dyads. RMSEs were lower in voting ensemble for regression, linear regression, and SVR than random forest, AdaBoost, and gradient boosted tree. The 5 most important predictors for infant birth weight were gestational age, fetal sex, preterm birth, mother's height, and pre-pregnancy BMI. After adding ultrasound-measured indicators of fetal growth into predictors, mother's height and pre-pregnancy BMI remained the most important predictors in predicting the outcome. Conclusion Mother's height and pre-pregnancy BMI were identified as important predictors for infant birth weight. Interpretable machine learning is a promising tool in the prediction of birth weight.
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影响因子：
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作者：
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通讯作者：
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影响因子：
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