Intelligent Prenatal Surveillance Architecture for Automated Fetal Health Classification and Preventive Decision Support

Prenatal care requires continuous assessment of fetal well-being for optimal outcomes; however, traditional monitoring methods do not provide frequent evaluations of fetal status and therefore may miss early signs of complications. We present a framework for real-time fetal health monitoring and predicting risk that incorporates data acquisition, preprocessing, and deep learning-based classification, thus improving clinical awareness and decision support. The physiological fetal parameters used to create input for the analytical model are collected from multiple sources and then cleaned, normalized, and handled for missing values prior to developing an analytical model. A Multi-Layer Perceptron (MLP) neural network is created to learn complex relationships among the processed data set and to classify the fetal condition into three categories: normal, suspect, or pathological. Real-time inference allows for rapid identification of potential fetal abnormalities; an automated recommendation process generates precautionary recommendations that are consistent with the predicted outcome. The combination of predictive analytics and continuous monitoring provides greater reliability than manual interpretation and reduces the chance of delayed intervention. Evaluation of the proposed learning system shows that it can provide consistent classification results and actionable insights. The proposed framework is expected to enhance maternal-fetal care through early detection of fetal problems, informed clinical responses to fetal problems, and improved methods of monitoring fetal well-being in modern healthcare settings.