Table 1.
Summary of current literature works for preterm birth prediction using AI.
Fig 1.
Preprocessing steps performed to build the final dataset.
Table 2.
Age group distribution according to the literature.
Table 3.
Selected attributes.
Table 4.
Attributes removed after preprocessing.
Fig 2.
Sampling scenarios used in our experiments.
Table 5.
Grid search hyperparameters.
Fig 3.
Overview for handling imbalanced data and training machine learning models.
Table 6.
Best hyperparameters when using undersampling.
Fig 4.
Models’ performance when using Undersampling to balance the training dataset.
Table 7.
Best hyperparameters when using oversampling.
Fig 5.
Models’ performance when using oversampling to balance the training dataset.
Table 8.
Best hyperparameters when using Hybridsampling double size.
Fig 6.
Models’ performance when using Hybridsampling double size to balance the training dataset.
Table 9.
Best hyperparameters when using Hybridsampling triple size.
Fig 7.
Hybridsampling triple size models performance.
Table 10.
Best hyperparameters when using Hybridsampling quadruple size.
Fig 8.
Models’ performance when using Hybridsampling quadruple size to balance the training dataset.