Successful evaluation of early term birth using hybrid ant colony

Abstract

Machine learning (ML) involves data mining, which is part of the science of data solving more problems. Machine learning application predicts the results depending on available data set. There are many predictive strategies available. The important method is dividing the most powerful predictions. Some of them predict results satisfactorily and some are accurately predicted. This research has carried out a process called as bagging based hybrid ensemble process that collects the accuracy of weak algorithms by combining many class dividers for improvement. This evaluation helps us to see the integration process that improves the accuracy in the result of preterm birth. This is not only the evaluation of weak separation algorithms, but also the use of algorithms using medical data, which predicts prematurely. This evaluation proves to be an effective method of classification by combining to improve the prediction accuracy of 96.2%.