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ORIGINAL RESEARCH

Establishment and Validation of a Machine Learning-Based Prediction Model for Termination of Pregnancy via Cesarean Section

, , , &
Pages 5567-5578 | Received 01 May 2023, Accepted 14 Nov 2023, Published online: 24 Nov 2023

Figures & data

Table 1 Comparisons of Baseline Characteristics Between Training Set and Validation Set

Figure 1 Flow chart of subject inclusion and analysis.

Figure 1 Flow chart of subject inclusion and analysis.

Figure 2 Screening of characteristic variables using Lasso.cv and Boruta. (A and B) Clinical feature selection using the Lasso.cv regression; (C) clinical feature selection using Boruta.

Figure 2 Screening of characteristic variables using Lasso.cv and Boruta. (A and B) Clinical feature selection using the Lasso.cv regression; (C) clinical feature selection using Boruta.

Figure 3 Construction of 4 models in training set.

Figure 3 Construction of 4 models in training set.

Figure 4 ROC, PRC, calibration curve, and DCA of RF in validation set. (A) ROC for RF prediction model on test set; (B) PRC for RF prediction model on test set; (C) Calibration curve for RF prediction model on test set; (D) DCA for RF prediction model on test set.

Figure 4 ROC, PRC, calibration curve, and DCA of RF in validation set. (A) ROC for RF prediction model on test set; (B) PRC for RF prediction model on test set; (C) Calibration curve for RF prediction model on test set; (D) DCA for RF prediction model on test set.

Figure 5 Sort graph of characteristic variables.

Figure 5 Sort graph of characteristic variables.

Figure 6 Univariate partial dependence profile.

Figure 6 Univariate partial dependence profile.

Figure 7 Break down profile of RF.

Figure 7 Break down profile of RF.