Laboratory markers predict pre-symptomatic PE. (IMAGE)
Caption
(A) The ROC curves of the preterm PE prediction models constructed by seven machine learning algorithms in the training set. (B) The ROC curves of the preterm PE prediction models constructed by seven machine learning algorithms in the testing set. (C) The SHAP summary plot of the AdaBoost model shows the effect of laboratory markers on the preterm PE prediction model. (D) Waterfall plot demonstrates the impact of specific laboratory markers on the predicted risk of preterm PE. (E) The ROC curves of the term PE prediction models constructed by seven machine learning algorithms in the training set. (F) The ROC curves of the term PE prediction models constructed by seven machine learning algorithms in the testing set. (G) The SHAP summary plot of the LightGBM model shows the effect of laboratory markers on the term PE prediction model. (H) Waterfall plot demonstrates the impact of specific laboratory markers on the predicted risk of term PE. PE, preeclampsia; ROC, receiver operating characteristic; SHAP, shapley additive explanations.
Credit
Yi Zhu, Yanqiu Zhang, Chao Huang, Sheng Zhang, Jun Cao, Nicole Miranda, Sarina Zhao, Yan Peng , Chao Yu, Bin Feng, Jieyu Jin, Qingqin Tang, Jiaming Fan, Longwei Qiao, Yuting Liang
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CC BY-NC-ND