Decision Tree Method Using for Fetal State Classification from Cardiotography Data

The motive of the investigation is analyzing the categorization of fetal state code from the Cardiographic data set based on decision tree method. Cardiotocography is one of the important tools for monitoring heart rate, and this technique is widely used worldwide. Cardiotocography is applied for diagnosing pregnancy and checking fetal heart rate state condition until before delivery. This classi cation is necessary to predict fetal heart rate situation which is belonging. In this paper, we are using three input attributes of training data set quoted by LB, AC, and FM to categorize as normal, suspect or pathological where NSPF variable is used as response variable. After drawing necessary analyzing into three variables we get the 19 nodes of classi cation tree and also we have measured every single node according to statistic, criterion, weights and values. The Cardiotocography Dataset applied in this study are received from UCI Machine Learning Repository. The dataset contains 2126 observation instances with 22 attributes. In this experiment, the highest accuracy is 98.7%. Overall, the experimental results proved the viability of Classication and Regression Trees and its potential for further predictions.