Abstract
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
References
- 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
Abstract
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.
Keywords
Kardiotokogram Karar destek sistemi Destek vektör makineleri Temel bileşen analizi Fetal iyilik hali
References
- 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
Details
| Subjects | Engineering |
|---|---|
| Journal Section | Research Articles |
| Authors | |
| Publication Date | December 16, 2016 |
| Submission Date | March 18, 2016 |
| Acceptance Date | November 27, 2016 |
| Published in Issue | Year 2016 Volume: 21 Issue: 2 |
Cite
Cited By
SAĞLIK HARCAMASININ TAHMİNİNDE MAKİNE ÖĞRENMESİ REGRESYON YÖNTEMLERİNİN KARŞILAŞTIRILMASI
Uludağ University Journal of The Faculty of Engineering
Songül ÇINAROĞLU
https://doi.org/10.17482/uumfd.338805
GÖMÜLÜ SİSTEM TABANLI BİR HATALI ÜRÜN TESPİT SİSTEMİ
Uludağ University Journal of The Faculty of Engineering
Raif Burak BAYRAM
https://doi.org/10.17482/uumfd.525696
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