Decision Trees Based Classification of Cardiotocograms Using Bagging Approach

Abstract

Cardiotocography (CTG) is a worldwide method used for recording fetal heart rate and uterine contractions during pregnancy and delivery. The consistent visual assessment of the CTG is not only time consuming but also requires expertise and clinical knowledge of the obstetricians. The inconsistency in visual evaluation can be eliminated by developing clinical decision support systems. During last few decades various data mining and machine learning techniques have been proposed for developing such systems. In present study, bagging approach in combination with three traditional decision trees algorithms (random forest, Reduced Error Pruning Tree (REPTree) and J48) has been applied to identify normal and pathological fetal state using CTG data. Studies show that decision trees algorithms and bagging have separately shown tremendous improvements in the classification of healthy and pathological subjects in medical domain. The parameters of classifiers were optimized before applying on the data sets. The ten folds cross validation is used for examining the robust of the classifiers. The degree of separation was quantified using Precision, Recall and F-Measure. At first full feature space have been analyzed using proposed bagging based decision trees algorithms. Then by using correlation feature selection - subset evaluation (cfs) method, a reduced feature space has been obtained and analyzed using proposed method. The overall classification accuracy of more than 90% has been obtained by the classifiers on the test set when full feature space is used. For all three performance measures, values greater than 0.90 has been achieved with full and reduced feature space. The proposed methodology showed better classification in both full and reduced feature space scenarios.