FETAL HEART RATE CLASSIFICATION AND COMPARATIVE ANALYSIS USING CARDIOTOCOGRAPHY DATA AND KNOWN CLASSIFIERS

Published 30 Jun 2019 •  vol 12  •  no 1  • 


Authors:

 

Razman Afridi, Department of Computer Science, City University of Science and Information Technology, Pakistan
Zafar Iqbal, Department of Computer Science, City University of Science and Information Technology, Pakistan
Muzammil Khan, Department of Computer Science, City University of Science and Information Technology, Pakistan
Arshad Ahmad, Department of Computer Science, City University of Science and Information Technology, Pakistan
Rashid Naseem, Department of Computer Science, City University of Science and Information Technology, Pakistan

Abstract:

 

The problem of fetal distress usually become one of the major reason of complication during child delivery. Fetal heart rate (FHR) is one of the pivotal ways to identify the occurrence of fetal distress. Cardiotocography (CTG) is the most widely practiced technique to record FHR. Improper analysis of CTG’s graph may lead to serious loss. This study presents six classification algorithms: Decision Tree (DT), K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF) and Naïve Bayes (NB), used for the classification of CTG data. To improve the performance of the classifiers, a co-relation based feature selection technique is employed over the dataset to remove the unnecessary attributes. The performance of the classification algorithms is evaluated using evaluation metrics: Accuracy, Precision, Recall, and F-measure. The results revealed that Naïve Bayes achieved 83.06% accuracy, 92.20% precision, 83.10% recall and 86.90% f-measure.

Keywords:

 

Cardiotocography; Decision Tree; K-Nearest Neighbors; Logistic Regression; Support Vector Machine; Random Forest; Naïve Bayes

References:

 

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Citations:

 

APA:
Afridi, R., Iqbal, Z., Khan, M., Ahmad, A., & Naseem, R. (2019). Fetal Heart Rate Classification and Comparative Analysis Using Cardiotocography Data and Known Classifiers. International Journal of Grid and Distributed Computing (IJGDC), ISSN: 2005-4262 (Print); 2207-6379 (Online), NADIA, 12(1), 31-42. doi: 10.33832/ijgdc.2019.12.1.03.

MLA:
Afridi, Razman, et al. “Fetal Heart Rate Classification and Comparative Analysis Using Cardiotocography Data and Known Classifiers.” International Journal of Grid and Distributed Computing (IJGDC), ISSN: 2005-4262 (Print); 2207-6379 (Online), NADIA, vol. 12, no. 1, 2019, pp. 31-42. IJGDC, http://article.nadiapub.com/IJGDC/vol12_no1/3.html.

IEEE:
[1] R. Afridi, Z. Iqbal, M. Khan, A. Ahmad, and R. Naseem, "Fetal Heart Rate Classification and Comparative Analysis Using Cardiotocography Data and Known Classifiers." International Journal of Grid and Distributed Computing (IJGDC), ISSN: 2005-4262 (Print); 2207-6379 (Online), NADIA, vol. 12, no. 1, pp. 31-42, Jun 2019.