The week's pick
Reseach Article
Classification of Cardiotocogram Data using Neural Network based Machine Learning Technique
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 47 - Number 14 |
| Year of Publication: 2012 |
| Authors: Sundar.c, M.chitradevi, G. Geetharamani |
10.5120/7256-0279
|
Sundar.c, M.chitradevi, G. Geetharamani . Classification of Cardiotocogram Data using Neural Network based Machine Learning Technique. International Journal of Computer Applications. 47, 14 ( June 2012), 19-25. DOI=10.5120/7256-0279
Abstract
Cardiotocography (CTG) is a simultaneous recording of fetal heart rate (FHR) and uterine contractions (UC). It is one of the most common diagnostic techniques to evaluate maternal and fetal well-being during pregnancy and before delivery. By observing the Cardiotocography trace patterns doctors can understand the state of the fetus. There are several signal processing and computer programming based techniques for interpreting a typical Cardiotocography data. Even few decades after the introduction of cardiotocography into clinical practice, the predictive capacity of the these methods remains controversial and still inaccurate. In this paper, we implement a model based CTG data classification system using a supervised artificial neural network(ANN) which can classify the CTG data based on its training data. According to the arrived results, the performance of the supervised machine learning based classification approach provided significant performance. We used Precision, Recall, F-Score and Rand Index as the metric to evaluate the performance. It was found that, the ANN based classifier was capable of identifying Normal, Suspicious and Pathologic condition, from the nature of CRG data with very good accuracy.
References
- Xiaojun Chen, Yunming Ye, Xiaofei Xu, Joshua Zhexue Huang , "A feature group weighting method for subspace clustering of high-dimensional data", Pattern Recognition 45 (2012) 434-446, Elsevier
- Shahad Nidhal, M. A. Mohd. Ali1 and Hind Najah, "A novel cardiotocography fetal heart rate baseline estimation algorithm", Scientific Research and Essays Vol. 5(24), pp. 4002-4010, 18 December, 2010
- ANA. KLIMEŠOVÁ, EVA OCELÍKOVÁ, Multidimensional Data Classification, Proceedings of the 10th WSEAS International Conference on AUTOMATION & INFORMATION, ISSN: 1790-5117, ISBN: 978-960-474-064-2
- Stirrat, Mills and Draycott, "Notes on Obstetrics and Gynaecology for the MRCOG, 5th Edition", 04 Aug 2003, ISBN: 9780443072239
- Diogo Ayres-de-Camposa, Cristina Costa-Santosb, Joa˜o Bernardesa, "Prediction of neonatal state by computer analysis of fetal heart rate tracings: the antepartum arm of the SisPorto1 multicentre validation study", European Journal of Obstetrics & Gynecology and Reproductive Biology 118 (2005) 52-60.
- http://www. academicjournals. org/SRE, ISSN 1992 – 2248 © 2010 Academic Journals.
- Antonia Costa, MD; Diogo Ayres-de-Campos, PhD; Fernada Costa, MD; Cristina Santos, MS; Joao Bernardes, PhD, "Prediction of neonatal academia by Computer analysis of fetal heart rate and ST event sibnals" 2009 AJOG – American Journal of Obstetrics and Gynecology.
- Ben Kao, Sau Dan Lee, Foris K. F. Lee, David W. Cheung, Wai-Shing Ho," Clustering Uncertain Data using Voronoi Diagrams and R-Tree Index" IEEE Transactions on Knowledge and Data Engineering, Vol. 22(9), pp. 1219 – 1233, sep 2010
- E. Ocelikova, D. Klimesova, " Bays Classifier in multidimensional data classification " 15th Int. Conference Process Control 2005, pp. 188-1 – 188-5. Strbske Pleso, Slovakia.
- E. Ocelikov?, J Krištof, "Classification of multispectral data" Zbornik radova, Volume 25, Number 1(2001).
- http://www-h. eng. cam. ac. uk/help/tpl/programs/ matlab. html.
- S. Anto, Dr. S. Chandramathi, "Supervised Machine Learning Approaches for Medical Data Set Classification – A Review" IJCST Nol. No. 2, Issue 4, pp. 234 – 240, Oct – Dec 2011, ISSN : 2229-4333.
- Frank, A. Asuncion, UCI Machine Learning Repository {http://archive. ics. uci. edu/ml}, 2010.
- Zhaohong Deng , Kup-Sze Choi , Fu-Lai Chung , Shitong Wang, Enhanced soft subspace clustering integrating within-cluster and between-cluster information, Pattern Recognition, v. 43 n. 3, p. 767-781, March, 2010 [doi>10. 1016/j. patcog. 2009. 09. 010]
- Hans-Peter Kriegel , Peer Kröger , Arthur Zimek, Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering, ACM Transactions on Knowledge Discovery from Data (TKDD), v. 3 n. 1, p. 1-58, March 2009 [doi>10. 1145/1497577. 1497578]
- http://www. mathworks. in/help/toolbox/nnet/ug/bss33y1-1. html.
- S. Angle Latha Mary, K. R. Shankar Kumar," Evaluation of Clustering Algorithm with Cluster Validation Metrics" European Journal of Scientific Research ISSN 1450-216X Vol. 69 No. 1 (2012), pp. 61-72
- https://sites. google. com/site/dataclusteringalgorithms/fuzzy-c-means-clustering-algorithm.
- http://home. dei. polimi. it/matteucc/Clustering/tutorial_html/cmeans. html.
- YI PENG, GANG KOU"A descriptive framework for the field of data mining and knowledge discovery" International Journal of Information Technology & Decision Making Vol. 7, No. 4 (2008) pp. 639–682.
- Michael Lloyd-Williams, "Discovering the hidden secrets in your data - the data mining approach to information", Information Research, {http://informationr. net/ir/3-2/paper36. html},Vol. 3 No. 2, September 1997.
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