KARDİOTOKOGRAM VERİSİNDEN FETAL İYİLİK HALİNİN BELİRLENMESİ İÇİN BİR KARAR DESTEK SİSTEMİ

Bu çalışmada kardiotogram verisinden fetal iyilik halinin belirlenmesi için bir karar destek sistemi önerilmiştir. Sistem En Küçük Kareler Destek Vektör Makineleri ve Temel Bileşen Analizi üzerinde temellendirilmiştir. Temel Bileşen Analizi yöntemi ile kardiotokogram veri kümesinin boyutu indirgenmiştir. Özellik boyutu indirgenen veri kümesi üzerinde En Küçük Kareler Destek Vektör Makineleri kullanılarak sınıflandırma işlemi gerçekleştirilmiştir. Önerilen karar destek sisteminin başarımı UCI Makine Öğrenmesi Ambarlarından alınan kardiotokogram veri kümesi üzerinde 10-katlı Çapraz Doğrulama tekniği kullanılarak incelenmiştir. Deneysel sonuçlar önerilen sistemin %98,74 sınıflandırma doğruluğuna, %98,86 duyarlılık oranına ve %98,73 özgüllük oranına sahip olduğunu göstermiştir.

Anahtar Kelimeler:

Kardiotokogram, Karar destek sistemi, Destek vektör makineleri, Temel bileşen analizi, Fetal iyilik hali

Decision Support System for Determination of Fetal Well-Being from Cardiotocogram Data

In this study, we propose a decision support system for assessment of fetal well-being from cardiotocogram data. The system is based on Principal Component Analysis and Least Squares Support Vector Machines. Principal Component Analysis is used for feature reduction of the cardiotocogram data set. Classification of the data set with reduced features is made by using Least Squares Support Vector Machines. Performance analysis of the proposed system is examined on the cardiotocogram data set availabe on UCI Machine Learning Repository by using 10-fold Cross Validation procedure. Experimetal results show that the proposed system has 98.74% classification accuracy, 98.86% sensitivity and 98.73% specificity rates

Keywords:

Cardiotocogram, Decision support system, Support vector machines, Principal component analysis, fetal well-being,

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Alfirevic, Z., Devane, D. ve Gyte, G.M.L. (2013). Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour, Cochrane Database of Systematic Reviews. doi: 10.1002/14651858.CD006066.pub2
Ayres-de-Campos, D., Bernardes, J., Garrido, A. ve diğ. (2000). SisPorto 2.0: a program for automated analysis of cardiotocograms. Journal of Maternal-Fetal and Neonatal Medicine, 9(5), 311–318. doi: 10.3109/14767050009053454
Boser, B.E., Guyon, I.M. ve Vapnik, V.N. (1992). A training algorithm for optimal margin classifiers, 5th Annual ACM Workshop on Computational Learning Theory, Pittsburgh, PA, USA, 144–152. doi: 10.1145/130385.130401
Fanelli, A., Magenes, G., Campanile, M. ve Signorini, M.G. (2013). Quantitative assessment of fetal well-being through CTG recordings: A new parameter based on phase-rectiﬁed signal average. IEEE Journal of Biomedical and Health Informatics, 17(5), 959-966. doi: 10.1109/JBHI.2013.2268423
Georgoulas, G., Stylios, C.D. ve Groumpos, P.P. (2006). Predicting the risk of metabolic acidosis for newborns based on fetal heart rate signal classification using support vector machines, IEEE Transactions on Biomedical Engineering, 53(5), 875–884. doi: 10.1109/TBME.2006.872814
Huang, M. ve Hsu, Y. (2012). Fetal distress prediction using discriminant analysis, decision tree, and artificial neural network, Journal of Biomedical Science and Engineering. doi: 10.4236/jbise.2012.59065
Jezewski, M., Czabanski, R. ve Leski, J. (2014). The influence of cardiotocogram signal feature selection method on fetal state assessment efficacy, Journal of Medical Informatics & Technologies, 23, 51-58.
Karabulut, E.M. ve Ibrikci, T. (2014). Analysis of cardiotocogram data for fetal distress determination by decision tree based adaptive boosting approach, Journal of Computer and Communications, 2(9), 32-37. doi: 10.4236/jcc.2014.29005
Kohavi, R. ve Provost, F. (1998). Glossary of terms, Machine Learning, 30(2-3), 271-274.
Krupa, N., MA, M.A., Zahedi, E., Ahmed, S. ve Hassan, F.M. (2011). Antepartum fetal heart rate feature extraction and classification using empirical mode decomposition and support vector machine, BioMedical Engineering Online. doi: 10.1186/1475-925X-10-6
Ocak, H. ve Ertunç, H.M. (2013). Prediction of fetal state from the cardiotocogram recordings using adaptive neuro-fuzzy inference systems, Neural Computing and Applications, 23(6), 1583-1589. doi: 10.1007/s00521-012-1110-3
Ocak, H. (2013). A medical decision support system based on support vector machines and the genetic algorithm for the evaluation of fetal well-being, Journal of Medical Systems, 37(9913), 1-9. doi: 10.1007/s10916-012-9913-4
Refaeilzadeh, P., Tang, L. ve Liu, H. (2009). Cross-validation. In Liu, L., Ozsu, M. T. (Eds.), Encyclopedia of Data Base Systems, 532–538. doi: 10.1007/978-0-387-39940-9_565
Ravindran, S., Jambek, A.B., Muthusamy, H. ve Siew-Chin, N. (2015). A novel clinical decision support system using improved adaptive genetic algorithm for the assessment of fetal well-being, Computational and Mathematical Methods in Medicine. doi: 10.1155/2015/283532
Sundar, C., Chitradevi, M. ve Geetharamani, G. (2012). Classification of cardiotocogram data using neural network based machine learning technique, International Journal of Computer Applications. doi: 10.5120/7256-0279
Sundar, C., Chitradevi, M. ve Geetharamani, G. (2013). An overview of research challenges for classification of cardiotocogram data, Journal of Computer Science. doi: 10.3844/jcssp.2013.198.206
Suykens, J.A.K. ve Vandewalle, J. (1999). Least squares support vector machine classifiers, Neural Processing Letters, 9(3), 293–300. doi: 10.1023/A:1018628609742
Van der Maaten, L., Postma, E. ve Van den Herik, J. (2009). Dimensionality reduction: a comparative review, Tilburg University, TiCC TR 2009-005.
Wang, X. ve Paliwal K.K. (2003). Feature extraction and dimensionality reduction algorithms and their applications in vowel recognition, Pattern Recognition, 36(2003), 2429-2439. doi: 10.1016/S0031-3203(03)00044-X
Yılmaz, E. ve Kılıkçıer, Ç. (2013). Determination of fetal state from cardiotocogram using LS-SVM with particle swarm optimization and binary decision tree, Computational and Mathematical Methods in Medicine. doi: 10.1155/2013/487179
Yılmaz, E. (2013). An expert system based on Fisher score and LSSVM for cardiac arrhythmia diagnosis, Computational and Mathematical Methods in Medicine. doi: 10.1155/2013/849674