Figure S7. Receiver operating characteristic curves for the prediction of LGA among multiparous women (pre-pregnancy) using elastic net, classification trees, random forest, gradient boosting, and neural networks. (PDF 200 kb
Additional file 1: Supplement Fig. 1. Calculations of sample size and model power
Table S3. Predicted breast cancer driver genes by the seven permutation models. Table S4. Predicted ...
Additional file 2: Table S1. Detailed list of potential predictors extracted from electronic health ...
Figure S6. Receiver operating characteristic curves for the prediction of LGA among primiparous wome...
Figure S8. Receiver operating characteristic curves for the prediction of LGA among multiparous wome...
Figure S4. Receiver operating characteristic curves for the prediction of SGA among multiparous wome...
Figure S1. Receiver operating characteristic curves for the prediction of SGA among primiparous wome...
Figure S2. Receiver operating characteristic curves for the prediction of SGA among primiparous wome...
Table S1. Predictors of fetal growth abnormalities and their use in the prediction models. (PDF 71 k...
Table S2. Training parameter grids and parameters used for five machine learning methods for the pre...
Table S3. Area under the curve in the training data for logistic regression and five machine learnin...
Additional file 1: Table S1. Basic characteristics of the cohort data. Table S2. The RMSE (g) of dif...
Figure showing the structure of the original Terneuzen Birth Cohort data, the broken stick data, and...
Associations between fetal sex and weightâ repeated measurements analyses. (PDF 86 kb
Additional file 1: Appendix: Figure 1. Random forest algorithm for prediction. Figure 2. Decision tr...
Additional file 1: Supplement Fig. 1. Calculations of sample size and model power
Table S3. Predicted breast cancer driver genes by the seven permutation models. Table S4. Predicted ...
Additional file 2: Table S1. Detailed list of potential predictors extracted from electronic health ...
Figure S6. Receiver operating characteristic curves for the prediction of LGA among primiparous wome...
Figure S8. Receiver operating characteristic curves for the prediction of LGA among multiparous wome...
Figure S4. Receiver operating characteristic curves for the prediction of SGA among multiparous wome...
Figure S1. Receiver operating characteristic curves for the prediction of SGA among primiparous wome...
Figure S2. Receiver operating characteristic curves for the prediction of SGA among primiparous wome...
Table S1. Predictors of fetal growth abnormalities and their use in the prediction models. (PDF 71 k...
Table S2. Training parameter grids and parameters used for five machine learning methods for the pre...
Table S3. Area under the curve in the training data for logistic regression and five machine learnin...
Additional file 1: Table S1. Basic characteristics of the cohort data. Table S2. The RMSE (g) of dif...
Figure showing the structure of the original Terneuzen Birth Cohort data, the broken stick data, and...
Associations between fetal sex and weightâ repeated measurements analyses. (PDF 86 kb
Additional file 1: Appendix: Figure 1. Random forest algorithm for prediction. Figure 2. Decision tr...
Additional file 1: Supplement Fig. 1. Calculations of sample size and model power
Table S3. Predicted breast cancer driver genes by the seven permutation models. Table S4. Predicted ...
Additional file 2: Table S1. Detailed list of potential predictors extracted from electronic health ...