Classification of Cardiotocography Records with Naïve Bayes

Yıl 2019, Cilt: 3 Sayı: 2, 105 - 110, 31.12.2019

Emre Avuçlu Abdullah Elen

Öz

Cardiotocography provides information about the fetal heart rate during pregnancy and childbirth, monitoring the uterine contractions and the physiological status of the fetus to identify hypoxia. Accurate information from these records can be used to estimate the pathological condition of the fetus. Thus, it allows early intervention by reporting any irreversible negative condition in the fetus. In this study, due to the importance of this subject, Naive Bayes machine learning algorithm can be used to diagnose the model developed. The result was 97.18% classification and 95.68% test success with Naive Bayes machine learning algorithm. The obtained data were presented in detail in the following sections.

Anahtar Kelimeler

Biomedical diagnostics, Machine learning algorithms, Fetal heart rate measurements

Classification of Cardiotocography Records with Naïve Bayes

Öz

Cardiotocography provides information about the fetal heart
rate during pregnancy and childbirth, monitoring the uterine contractions and
the physiological status of the fetus to identify hypoxia. Accurate information
from these records can be used to estimate the pathological condition of the
fetus. Thus, it allows early intervention by reporting any irreversible
negative condition in the fetus. In this study, due to the importance of this
subject, Naive Bayes machine learning algorithm can be used to diagnose the
model developed. The result was 97.18% classification and 95.68% test success
with Naive Bayes machine learning algorithm. The obtained data were presented
in detail in the following sections.

Anahtar Kelimeler

Biomedical diagnostics, Machine learning algorithms, Fetal heart rate measurements.

Kaynakça

• [1] Beyan, E., İntrapartum Fetal Kalp Hızı Traselerinin Neonatal Sonuçlar İle İlişkisi, İzmir İli Kamu Hastaneleri Birliği Kuzey Genel Sekreterliği İzmir Tepecik Eğitim Araştırma Hastanesi 3.Kadın Hastalıkları Ve Doğum Kliniği, Uzmanlık Tezi, İzmir, 2013.[2] Yüksel, M. U., Fetal Kalp Hızı Monitörizasyon Sistemi (FHRms) Ve Mobil Entegre Doppler (M-Doppler) Cihazının Geliştirilmesi, İstanbul Aydın Üniversitesi Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Ana Bilim Dalı, İstanbul, 2017.[3] Kanmaz, H., Mobil Fetal Kalp Hızı Monitörizasyon Sistemi (FHRms) Geliştirilmesi, İstanbul Aydın Üniversitesi Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Ana Bilim Dalı, İstanbul, 2018.[4] Tekin gündüz, S., kurtuldu, A., & Türkkan, I. Ş. I. K, “Sağlık Hizmetlerinde Eşitsizlik ve Etik”, Aksaray Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 8(4): 32-43, 2017.[5] Zach, L., Chudáček, V., Kužílek, J., Spilka, J., Huptych, M., Burša, M., & Lhotská, L., “Mobile CTG—Fetal heart rate assessment using Android platform”, In Computing in Cardiology, pp. 249-252. IEEE, 2011.[6] Andersson, S. U. S. A. N. N. E., Acceleration and deceleration detection and baseline estimation. Göteborg: Chalmers University of Technology, 2011.[7] UnbornHeart, Available from: http://www.unbornheart.com/, Accessed date: May 2018.[8] D. Ayres-de-Campos, C. Y. Spong, E. Chandraharan, “FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography”, Int J Gynaecol Obstet, vol. 131, pp. 13-24, Oct 2015.[9] Jirı Spilka, V Chudáček, Michal Koucký, Lenka Lhotská, Michal Huptych, Petr Janků, George Georgoulas, Chrysostomos Stylios, “Using nonlinear features for fetal heart rate classification”, Biomedical Signal Processing and Control, vol. 7, pp. 350-357, 2012.[10] R. Czabanski, M. Jezewski, K. Horoba, J. Jezewski, and J. Leski, “Fuzzy Analysis of Delivery Outcome Attributes for Improving the Automated Fetal State Assessment”, Applied Artificial Intelligence, vol. 30, pp. 556-571, 2016.[11] H. Sahin and A. Subasi, “Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques”, Applied Soft Computing, vol. 33, pp. 231-238, 2015.[12] C. Buhimschi, M.B. Boyle, G.R. Saade, R.E., “Garfield Uterine activity during pregnancy and labor assessed by simultaneous recordings from the myometrium and abdominal surface in the rat”, Am. J. Obstet. Gynecol., 178 (4), pp. 811-822, 1998.[13] C. Buhimschi, M.B. Boyle, R.E., “Garfield Electrical activity of the human uterus during pregnancy as recorded from the abdominal surface”, Obstet. Gynecol., 90 (1): pp. 102-111, 1997.[14] C. Buhimschi, R.E. Garfield, “Uterine contractility as assessed by abdominal surface recording of electromyographic activity in rats during pregnancy”, Am. J. Obstet. Gynecol., 174 (2): pp. 744-753, 1996.[15] J.S. Richman, J.R. Moorman, “Physiological time-series analysis using approximate entropy and sample entropy”, Am. J. Physiol. - Hear. Circ. Physiol., 278 (6), 2000. [16] E. Blinx, K.G. Brurberg, E. Reierth, L.M. Reinar, P. Oian, “ST waveform analysis versus Cardiotocography alone for intrapartum fetal monitoring: a systematic review and meta-analysis of randomized trials”, Acta Obstet. Gynancelogica Scand., 95 (1):pp. 16-27, 2016[17] M.E. Menai, F.J. Mohder, F. Al-mutairi “Influence of feature selection on Naïve Bayes classifier for recognizing patterns in cardiotocograms”, J. Med. Bioeng., 2 (1): pp. 66-70, 2013.[18] E.M. Karabulut, T. Ibrikci, “Analysis of cardiotocogram data for fetal distress determination by decision tree based adaptive boosting approach”, J. Comput. Commun., 2 (9): pp. 32-37, 2014.[19] J. Spilka, G. Georgoulas, P. Karvelis, V. Chudacek “Discriminating normal from ‘Abnormal’ pregnancy cases using an automated FHR evaluation”, Method Artif. Intell. Methods Appl., 8445, pp. 521-531, 2014.[20] B. Chudacek, J. Spilka, M. Bursa, P. Janku, L. Hruban, M. Huptych, L. Lhotska “Open access intrapartum CTG database BMC Pregnancy”, Childbirth, 14 (16): pp. 1-12, 2014.[21] J. Spilka, V. Chudacek, M. Koucky, L. Lhotska, M. Huptych, P. Janku, G. Georgoulas, C. Stylios, “Using nonlinear features for fetal heart rate Classification”, Biomed. Signal Process. Control, 7 (4): pp. 350-357, 2012.[22] Ayres-de Campos, Bernardes J, Garrido A, Marques-de-Sá J, Pereira-Leite L. “SisPorto 2.0 A program for Automated Analysis of Cardiotocograms”, J Matern Fetal Med., 5: pp. 311-318, 2000.[23] Web site, Available from: https://archive.ics.uci.edu/ml/datasets/Cardiotocography#, Access date:10.7.2019.[24] J. Spilka, G. Georgoulas, P. Karvelis, V. Chudacek, “Discriminating normal from ‘Abnormal’ pregnancy cases using an automated FHR evaluation”, Method Artif. Intell. Methods Appl., 8445, pp. 521-531, 2014.[25] D. Rindskopf, W. Rindskopf, “The value of latent class analysis in medical diagnosis”, Stat. Med., 5 (1), pp. 21-27, 1986.