Received May 17, 2020, accepted May 29, 2020, date of current version June 11, 2020. Digital Object Identifier 10.1109/ACCESS.2020.2999899 Performance Analysis of Classification Algorithms on Birth Dataset SYED ALI ABBAS 1 , AQEEL UR REHMAN 2 , FIAZ MAJEED 3 , ABDUL MAJID 1 , M. SHERAZ ARSHED MALIK 4 , ZAKI HASSAN KAZMI 1 , AND SEEMAB ZAFAR 5 1 Department of Computer Science and Technology, The University of Azad Jammu and Kashmir, Muzaffarabad 13100, Pakistan 2 Department of Electronics and Information Engineering, Southwest University, Chongqing 400715, China 3 Department of Information Technology, University of Gujrat, Gujrat 50700, Pakistan 4 Department of Information Technology, Government College University Faisalabad, Faisalabad 38000, Pakistan 5 Department of Gynecology and Obstetrics, Abbas Institute of Medical Sciences Hospital, Muzaffarabad 13100, Pakistan Corresponding author: Aqeel Ur Rehman (rehmancqu@gmail.com) ABSTRACT Generating intuitions from data using data mining and machine learning algorithms to predict outcomes is useful area of computing. The application area of data mining techniques and machine learning is wide ranging including industries, healthcare, organizations, academics etc. A continuous improvement is witnessed due to an ongoing research, as seen particularly in healthcare. Several researchers have applied machine learning to develop decision support systems, perform analysis of dominant clinical factors, extraction of useful information from hideous patterns in historical data, making predictions and disease classification. Successful researches created opportunities for physicians to take appropriate decision at right time. In current study, we intend to utilize the learning capability of machine learning methods towards the classification of birth data using bagging and boosting classification algorithms. It is obvious that differences in living styles, medical assistances, religious implications and the region you live in collectively affect the residents of that society. This motive has encouraged the researchers to conduct studies at regional levels to comprehensively explore the associated medical factors that contribute towards complications among women during pregnancy. The current study is a comprehensive comparison of bagging and boosting classification algorithms performed on birth data collected from the government hospitals of city Muzaffarabad, Kashmir. The experimental tasks are carried out using caret package in R which is considered an inclusive framework for building machine learning models. Accuracy based results with different evaluation measures are presented. Bagging functions including Adabag and BagFda performed marginally better in terms of accuracy, precision and recall. Improvements are observed in comparison to previous study performed on same dataset. INDEX TERMS Cesarean-section, machine learning, bagging, classification, boosting, health care. I. INTRODUCTION Knowledge engineering and machine learning helps to simulate decision making activities in various fields like healthcare, manufacturing, image processing, prediction, cus- tomer services etc. Numerous machine learning algorithms are used for prediction and data classification. Healthcare organizations are using applications of information technol- ogy and machine learning in order to optimize the activ- ities of operational decisions and respond to the needs of physicians. Machine learning helps in emergency medical situations, decision making activities, general primary care, The associate editor coordinating the review of this manuscript and approving it for publication was Alicia Fornés . and also help physicians to choose best operation methods when it is hard to predict the outcomes in diagnosing the patients [1]. Many researches are focused on the development of decision support systems that assist physicians to gain insights and predict different outcomes relevant to the area of interest [35]. However, some subtle areas seek attention that is major cause of death in low income countries for example pregnancy complications. These complications may expose expecting mother to diseases [38], [40] and cause situations demanding birth by caesarean section (C-section). A C-section is a method when the expected child is deliv- ered unnaturally by going through some surgical procedure. A C-section becomes inevitable in case of multiple babies in uterus, baby in traverse position or in breech, previous 102146 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ VOLUME 8, 2020