Abstract
Machine learning and data mining are being used in different fields like data analysis, prediction, image processing, etc., and particularly in healthcare. Over the past decade, several types of research have been carried out focusing on machine learning and data mining application to generate intuitions from historical data and make predictions about the results. Machine learning algorithms play a vital role in improving healthcare systems due to continuous research in machine learning applications. Several researchers have used algorithms of machine learning to develop systems for decision support, analyze clinical aspects, use historical data to extract useful information, make future predictions and categorize diseases, etc. to help physicians make better decisions. In this study, we used an ensemble modeling voting technique for the classification of the birth dataset. Ensemble models combine individual machine learning algorithms to improve the accuracy by predicting from the combined output of the base classifiers. Gradient boosting classifier (GBC), random forest (RF), bagging classifier (BC), and extra trees classifier (ETC) were used as base learners for making a voting ensemble model for the classification of the birth dataset. The results produced have shown that the voting classifier of support vector machine (SVM), random forest (RF), extra trees classifier, and bagging classifier has given the best results with the proportion of 94.78%, gradient boosting classifier has 84.39% accuracy, the random forest has 94.26% accuracy, extra trees classifier have 94.02% accuracy and bagging classifier has 93.65% accuracy. The accuracy achieved by ensemble modeling is far higher than the machine learning algorithms. Ensemble models increase the accuracy of machine learning algorithms by reducing variance and classification errors. The development of such a system will not only help health organizations to take effective measures to improve the maternal health assessment process but will also open the doors for interdisciplinary research in two different fields in the region.