Fetal Health Prediction: Comparative Analysis of Machine Learning Models

Monitoring the health of the fetus is crucial to the mother's and the child's well-being. Even though cardiotocography (CTG) is used to assess fetal conditions, interpretation is usually done manually, which can be inaccurate and error-prone. This study investigates automating the classification of fetal health using machine learning, which would improve the process's efficiency and dependability. To improve predictive performance, we create hybrid models integrating Support Vector Machine (SVM) with Bagging and Multi-Layer Perceptron (MLP) with Bagging which achieved an accuracy of 0.99. Our goal is to correctly identify fetal health as normal, suspicious, or pathological by pre-processing CTG data, choosing important features, and training these models. Our method makes use of ensemble learning strategies to enhance dependability and facilitate clinical judgment. The findings demonstrate how AI- driven techniques can provide quicker and more objective evaluations, helping medical practitioners make early diagnoses and take timely action.