Improving the Performance of Classifiers by Ensemble Techniques for the Premature Finding of Unusual Birth Outcomes from Cardiotocography

Abstract

In the present era of the medical field, the mortality rate of the infant increased by 1/3 percentage in a year. Even though we have modernized and have great expertise in the medical domain, we failed to control the infantile mortality rate. So continuous monitoring of the fetus during pregnancy is important. If there is any complication in the growth of the fetus, then the patient is put into the appropriate examination and medication proposed by the physician. In this paper, we propose methods to control the infant mortality rate in the early stage of pregnancy. Fetal heart rate of 2126 patients was collected and developed as datasets. Then with these datasets, we developed machine learning classifier models to classify the normal, suspect, and pathologic cases using the Decision tree, Naive Bayes, Random forest, and K-nearest neighbors. We divided the datasets into training dataset and testing dataset. Then the base classifier model was created using training datasets, and then the same models were verified by appending the test datasets. We improved the techniques and efficacy of the base classifiers by ensemble methods such as bagging and boosting. Finally, the datasets were classified as normal, suspect, and pathological cases with an improved accuracy of 96.617% with the help of the random forest classifier.