CTG-Based Fetal Health Prediction: A Comparative Study of Machine Learning Models

This study explores the use of machine learning approaches for fetal health classification utilizing Cardiotocogram (CTG) data to improve prenatal treatment and maternal-fetal health. The study systematically examines five machine learning algorithms: Random Forest, Gradient Boosting, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Logistic Regression, using an extensive record with 2,126 well-annotated CTG recordings. This work is peculiar since it uses a wide range of feature extraction and preprocessing techniques to ensure high model correctness and dependability. With a 95% accuracy rate, the Random Forest algorithm proven to be the most effective, surpassing the other models by far. This study emphasizes how machine learning, especially ensemble approaches, may effectively predict health problems in fetuses. The study lays the groundwork for future developments in fetal health detection using cutting-edge technologies and machine learning algorithms by emphasizing interpretability and transparency.