Exploring the Insights of Bat Algorithm-Driven XGB-RNN (BARXG) for Optimal Fetal Health Classification in Pregnancy Monitoring

Pregnancy monitoring plays a pivotal role in ensuring the well-being of both the mother and the fetus. Accurate and timely classification of fetal health is essential for early intervention and appropriate medical care. This work presents a novel method for classifying fetal health optimally by combining the Bat Algorithm (BA) in an effective manner with a hybrid model that combines Recurrent Neural Networks (RNN) and Extreme Gradient Boosting (XGB). The Bat Algorithm, inspired by the echolocation behaviour of bats, is employed to optimize the hyperparameters of the XGB-RNN hybrid model. This enables the model to adapt dynamically to the complexities of fetal health data, enhancing its performance and predictive accuracy. The XGB-RNN hybrid model is designed to capitalize on the strengths of both algorithms. XGB provides superior feature selection and gradient boosting capabilities, while RNN excels in capturing temporal dependencies in the data. This approach effectively deals with the difficulties involved in classifying fetal health in the context of pregnancy monitoring by combining these approaches. Python is used to implement the proposed framework. To validate the performance of the proposed approach, extensive experiments were conducted on a comprehensive dataset comprising a wide range of physiological parameters related to fetal health. When it comes to fetal health, BAT Algorithm's XGB-RNN (BARXG) performs outstandingly, greater than other classifiers in terms of accuracy, sensitivity, and specificity. The proposed BARXG model has greater accuracy (98.2%) than existing techniques, which include SVM, Random Forest Classifier, LGBM, Voting Classifier, and EHG.