Improve Classification Quality of Fetal Status from Cardiotocogram Data by Using Machine Learning

In pregnance duration, there may be various reasons which may cause a problem which may lead to mortality in the newborn. To minimize such accidents, one of the most significant tool to analyze the health of the fetal in the womb is by doing a Cadiotocography (CTG). CTG is one of the most commonly used techniques to assess the health of the fetus during labor in medicine. The main purpose of monitoring the fetus with this electronic measurement is to prevent risks to the fetus. To enhance this process and assist both patients and physicians in identifying the problem, we propose the capabilities of Machine learning to address this challenge. Machine learning is a field that has been widely applied in life, especially in medicine. This technique has brought great advances in diagnosis, treatment and prognosis of diseases. We study machine learning techniques to classify fetal status through CTG data by This paper is used classification methods such as Decision Tree (DT) and Random Forest (RF). The analysis results show that these methods have good classification quality with high accuracy. In particular, the Random Forest method gives the highest results with an accuracy of 99.19%.