Fetal Health State Monitoring Using Decision Tree Classifier from Cardiotocography Measurements

Perinatal Mortality is an alarming issue in the world which needs immediate attention for the sustainable growth of nation. To address this issue, both intrapartum and antepartum fetal health state monitoring is essential. Fetal and maternal risk can be assessed by monitoring the fetal heart rate. Cardiotocography records the fetal heart rate and Uterine contractions of mother simultaneously. This signal data can support the physicians to understand the fetal health status. In this paper, an efficient method to predict the high-risk pregnancy based on the fetal health status using CART is proposed. A CTG datasets having 2126 recordings from UCI machine learning repository is used for classification. 5- fold cross validation is done, and the proposed methodology using CART was quantified using precision, recall and F-score.