Machine Learning in Fetal Health: Improving ECG Analysis with Random Forest

Abstract

Ensuring fetal health throughout pregnancy is paramount for a successful delivery and a healthy newborn. Fetal electrocardiography (ECG) offers valuable insights into fetal cardiac health, but the complexity and variability of ECG data present interpretation challenges. This study investigates the application of machine learning, specifically Random Forest (RF), for fetal ECG prediction and analysis. RF’s ability to handle large datasets and deliver accurate results makes it a promising solution. The study compares the RF-based approach with established machine-learning techniques like Artificial Neural Network, Support Vector Machines, and Recurrent Neural Network. The comparison demonstrates the superior performance of this method in terms of accuracy, robustness, and reliability. The paper meticulously details the methodology, algorithm implementation, and comparative results. It emphasizes the advantages of Random Forest for fetal ECG analysis and its potential as a future clinical tool. Random Forest emerges as a promising approach for fetal ECG analysis, potentially improving clinical practice and fetal well-being.