© 2022, AJCSE. All Rights Reserved 71 RESEARCH ARTICLE Impact of Classification Algorithms on Cardiotocography Dataset for Fetal State Prediction D. Sudharson 1 , K. Vignesh 2 , Aman Kumar Dubey 2 , K. Mukilan 3 , M. K. Nanda Kizore 3 , S. Abishek Ram 3 1 Department of AI and DS, Kumaraguru College of Technology, Coimbatore, Tamil Nadu, India, 2 Department of MBA, Kumaraguru College of Technology, Coimbatore, Tamil Nadu, India, 3 Department of AI and DS, Kumaraguru College of Technology, Coimbatore, Tamil Nadu, India Received on: 12/02/2022; Revised on: 29/03/2022; Accepted on: 30/04/2022 ABSTRACT Monitoring of fetal heart rate and fetal health is done by cardiotocography (CTG). Obstetricians can observe CTG records and make life-saving decisions. The ability to go throh all the data points is fairly challenging. One possible solution is to use clinical decision making systems. The selection of these systems is made possible by choosing the best classifier, in this paper we compare four simple classifiers (K Nearest Neighbors, Decision Tree, Support Vector Machine, Naive Bayes). To improve accuracy, the dataset is split based on “Outlier Removal” and “Feature Selection”. Key words: Cardiotocography, Classifcation, Decision tree, Feature selection, Outlier removal INTRODUCTION About 295 000 women die globally due to “Maternal mortality”. Sub-Saharan Africa and Southern Asia accounted for rohly 86% of the estimated global maternal deaths in 2017. This shows the inequality in health facilities across the world. The deaths could be prevented by observing the cardiotocography (CTG) records and taking timely actions during pregnancy. CTG records fetal heart rate (FHR) and uterine contractions (UC) during pregnancy using an ultrasound transducer which is placed on the mother’s abdomen, this method is done to check fetal well- being typically in the third trimester (27–40 weeks). Babies at risk of hypoxia (lack of oxygen) are mostly monitored using CTG to avoid death and long-term disablement due to lack of oxygen during the time of delivery. The data in CTG are interpreted to ensure precise prediction of fetal well-being and prepare the mother for delivery. Obstetricians manually observe FHR patterns during this process by CTG. Manually viewing CTG recordings is daunting and challenging. Optimal classifier-based clinical decision-making (CDM) systems offer a viable solution for finding patterns in CTG datasets. Four classifiers were used Address for correspondence: Dr. D. Sudharson E-mail: sudharsondorai.ads@kct.ac.in in this work: K Nearest Neighbors (KNN), Decision Trees (DT), Support Vector Machines (SVM), and Naive Bayes (NB). These classifiers were used to predict a target variable NSP that indicates fetal status (N = Normal, S = Suspicious, and P = Pathological). Optimal classifiers for CDM systems have been proposed. LITERATURE SURVEY The electronic monitoring of FHR was introduced way back in 1987, but due to a lack of proper records, the development of clinical decision-making (CDM) algorithms was not developed. CDM algorithms and systems were gradually improved using data mining techniques thereby predicting the state of the fetus based on uterine constraints and accelerations of the fetus per second. Research shows that clustering medical data have improved predictability. [1] The CTG dataset which was used in the research was used along with Sisporto which does automatic analysis of CTG tracings, which was introduced by Bernades. [2,3] Comparative analysis done with four classifiers on the CTG dataset with 21 attributes and a reduced CTG showed that the latter produces the best results. [4,5] On the surface, ANN classifiers seem to provide good average performance but in individual runs, the performance varies wildly. [6,7] Another method to improve the prediction accuracy of a model is to introduce data sample Available Online at www.ajcse.info Asian Journal of Computer Science Engineering 2022;7(2):71-76 ISSN 2581 – 3781