Efficient Fetal Health Prediction using Machine Learning

Abstract

The growth of technology in our day-to-day enterprise with advanced machines are outstanding through machine learning involving both machine learning and deep learning all over the world. Fetal monitoring during pregnancy time is the most important to save the life of the mother as well as the child. In this project, we present a ML technique that is used to measure the fetal heart rate during the time of pregnancy. The major component used for this detection is Fetal Digital stethoscope sensor which is to be placed on the abdomen of the pregnant and the signals are processed by the micro-controller used and the accurate fetal heart rate. This system is very flexible and low cost helps the patient to monitor the fetal heart rate in home. We will use ML method for our project. In this paper Fetal health is predicted by algorithms namely Decision Tree (DT) as existing and Recurrent Neural Network (RNN) as proposed and compared in terms of accuracy. From our work we can prove that our proposed RNN works better than other existing DT algorithm.