<![CDATA[Monitoring fetal health is a crucial aspect of pregnancy, requiring accurate and efficient methods for early detection of potential complications. This study aims to develop a fetal health classification system using the Support Vector Machine (SVM) algorithm. The data analyzed includes various fetal physiological parameters obtained through routine examinations, such as heart rate, fetal movements, and other relevant indicators. SVM was chosen due to its capability to handle non-linear data and its high classification accuracy. The classification process involves data preprocessing, feature selection, model training, and performance evaluation using metrics such as accuracy, precision, recall, and F1-score. The results indicate that SVM can effectively classify fetal health conditions with high accuracy, making it a promising diagnostic support tool for medical professionals. This study contributes to maternal and fetal healthcare by offering a machine learning-based approach that enhances the effectiveness of fetal health monitoring systems. ]]>
