Integrated UnderOversampling Responsive based Fetal Health Prediction using Cardiotocographic Data

Abstract

With the current enhancement of advancement towards pharmaceutical, particular ultrasound strategies are open to discover the fetal thriving. It is analyzed with assorted clinical parameters with 2-D imaging and other test. In any case, success want of fetal heart still remains an open issue due to unconstrained works out of the hatchling, the minor heart assess and lacking of information in fetal echocardiography. The machine learning techniques can discover out the classes of fetal heart rate which can be utilized for prior assessing. With this background, we have utilized Cardiotocographic Fetal heart rate dataset removed from UCI Machine Learning Store for predicting the fetal heart rate health classes. The Prediction of fetal health rate are achieved in six ways. Firstly, the data set is preprocessed with Feature Scaling and missing values. Secondly, exploratory data investigation is done and the dispersion of target feature is visualized. Thirdly, the raw data set is fitted to all the classifiers and the performance is analysed before and after feature scaling. Fourth, the raw data set is subjected to undersampling methods like NeighbourhoodCleaningRule, OneSidedSelection, RandomUnderSampler, TomekLinks, SmoteENN and SmoteTomek. Fifth, the undersampled dataset by above mentioned methods are fitted to all the classifiers and the performance is analyzed before and after feature scaling. Sixth, performance analysis is done using metrics like Precision, Recall, F-score, Accuracy and running time. The execution is done using python language under Spyder platform with Anaconda Navigator. Experimental results shows that the Random Forest classifier tends to retain 98% before and after feature scaling for the undersampling with NeighbourhoodCleaningRule, SmoteENN and SmoteTomek methods comparing to other methods.