Fetal Health Classification Using Supervised Learning Approach

Abstract

Fetal Health monitoring is important to reduce or minimize the mortality of both mother and child. This paper presents a study on a dataset of 2126 records on features extracted from cardiotocography exam with 21 attributes including baseline value accelerations, fetal movement, uterine contractions, light, severe and prolonged decelerations, abnormal short-term variability, the mean value of short-term variability, percentage of time with abnormal long-term variability, the mean value of long-term variability, histogram width, min, max, number of peaks, number of zeroes, mode, mean, median, variance, and tendency. This paper will be using Supervised Machine Learning to compare and classify the data set using K-NN, Linear SVM, Naive Bayes, Decision Tree (J4S), Ada Boost, Bagging and Stacking. Lastly, Bayesian networks are then developed and compared with the other classifier. By comparing all of the classifiers, classifier Ada Boost with sub-model Random Forest has the highest accuracy 94.7% with k = 10.