Fetal Status Classification with Heart Rate Tracker and CTG by using Decision Tree




Fetal status, Classification, CTG, Heart rate, Contraction of the Uterus, Decision tree


The important to care for the pregnant women in both the prenatal and labor stages. This is the period for which the mortality rate is approximately one third of all births. This article purposes a primary classification of fetal state and create a fetal status classification model to help monitoring the risk that may occur with the fetus. Fetal health checks are necessary in the prenatal period to find the fetus at risk for neurological injury and death of the baby. A condition that can be prevented will be prevented from occurring. In this article, we present an intelligent model for identifying fetal status. The cardiotocography dataset by J. Bernardes which consists of 23 attributes and 2,126 samples and divided into 3 classes as normal, suspect and pathologic. In the experimental for the best. We designed the experimental with 10-fold cross validation and compared the group of decision tree classifier as Decision Stump, Hoeffding Tree, J48, Logistic Model Tree, Random Forest, Random Tree and REP Tree. The results show J48 algorithm, which gives the highest accuracy of 92.79%. The results of the experiment indicate that this algorithm is easy to understand. The operation and the process are not complicated but they can effectively classification fetal status from dataset.


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How to Cite

CHUMUANG, N. (2021). Fetal Status Classification with Heart Rate Tracker and CTG by using Decision Tree. Science Technology and Innovation Journal, 2(4), 36–46. Retrieved from https://li01.tci-thaijo.org/index.php/stij/article/view/251591



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