  Fetal health is a critical concern during pregnancy as it can impact the
well-being of both the mother and the baby. Regular monitoring and timely
interventions are necessary to ensure the best possible outcomes. While there
are various methods to monitor fetal health in the mother's womb, the use of
artificial intelligence (AI) can improve the accuracy, efficiency, and speed of
diagnosis. In this study, we propose a robust ensemble model called ensemble of
tuned Support Vector Machine and ExtraTrees (ETSE) for predicting fetal health.
Initially, we employed various data preprocessing techniques such as outlier
rejection, missing value imputation, data standardization, and data sampling.
Then, seven machine learning (ML) classifiers including Support Vector Machine
(SVM), XGBoost (XGB), Light Gradient Boosting Machine (LGBM), Decision Tree
(DT), Random Forest (RF), ExtraTrees (ET), and K-Neighbors were implemented.
These models were evaluated and then optimized by hyperparameter tuning using
the grid search technique. Finally, we analyzed the performance of our proposed
ETSE model. The performance analysis of each model revealed that our proposed
ETSE model outperformed the other models with 100% precision, 100% recall, 100%
F1-score, and 99.66% accuracy. This indicates that the ETSE model can
effectively predict fetal health, which can aid in timely interventions and
improve outcomes for both the mother and the baby.
