Klasifikasi Fetal Cardiotocography Menggunakan Pendekatan Boosting Classifier
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Published:
Nov 29, 2023
Keywords:
Fetal, Cardiotocography, Outlier detection, Selection feature, Boosting
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Abstract
Fetal cardiotography, sebuah alat penting dalam pemantauan kesehatan janin selama kehamilan. Fetal cardiotography memberikan informasi vital tentang kesehatan janin, termasuk detak jantung janin dan aktivitas gerakan janin. Ini membantu dokter dan perawat untuk memantau kesehatan janin secara berkala selama kehamilan. Dengan mengamati pola detak jantung janin, fetal Cardiotocography dapat membantu mendeteksi dini masalah kesehatan janin, seperti hipoksia (kurangnya oksigen), ketidakseimbangan cairan ketuban, atau masalah dengan plasenta. Integrasi antara penggunaan machine learning untuk mendukung diagnosa dokter terhadap kondisi detak jantung janin ini menjadi sangat diperlukan. Adanya sebuah sistem berbasis AI menjadikan permasalahan subjektifitas dalam hasil diagnosa dapat diminimalisir. Pada penelitian ini mengembangkan sebuah model machine learning yang berbasiskan pada teknik boosting. Kombinasi antara outlier detection dan feature selection dianalisis dan kemudian diujicobakan pada tiga jenis algoritma boosting. Hasil dari eksperimen menunjukkan bahwa kombinasi antara local outlier factor, chi aquare dan extrem gradient boosting mampu memberikan performa terbaik yaitu dengan nilai akurasi sebesar 99.3%, presisi dengan 99.1%, recall 99.1% dan F-Measure sebesar 99.1%.
Article Details
How to Cite
Pradipta, G. A., & Putu Desiana Wulaning Ayu. (2023). Klasifikasi Fetal Cardiotocography Menggunakan Pendekatan Boosting Classifier. Jurnal Sistem Dan Informatika (JSI), 18(1), 103-110. https://doi.org/10.30864/jsi.v18i1.594
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M. S. Iraji, “Prediction of fetal state from the cardiotocogram recordings using neural network models,” Artif Intell Med, vol. 96, pp. 33–44, May 2019, doi: 10.1016/j.artmed.2019.03.005.
K. Bache and M. Lichman, “UCI Machine Learning Repository,” UCI Machine Learning Repository [http://archive. ics. uci. edu/ml].
L. A. Zhu et al., “Fetal physiology cardiotocography training, a regional evaluation,” J Gynecol Obstet Hum Reprod, vol. 50, no. 6, Jun. 2021, doi: 10.1016/j.jogoh.2020.102039.
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G. A. Pradipta and P. D. Wulaning Ayu, “Fetal weight prediction based on ultrasound image using fuzzy C means clustering and Itterative Random Hough Transform,” in Proceedings - 2017 1st International Conference on Informatics and Computational Sciences, ICICoS 2017, Institute of Electrical and Electronics Engineers Inc., Oct. 2017, pp. 71–76. doi: 10.1109/ICICOS.2017.8276340.
M. Ghelich Oghli et al., “Automatic fetal biometry prediction using a novel deep convolutional network architecture,” Physica Medica, vol. 88, pp. 127–137, Aug. 2021, doi: 10.1016/j.ejmp.2021.06.020.
X. Wang, W. Wang, and X. Cai, “Automatic measurement of fetal head circumference using a novel GCN-assisted deep convolutional network,” Comput Biol Med, vol. 145, Jun. 2022, doi: 10.1016/j.compbiomed.2022.105515.
Hu Ricky, Singla Rohit, Yan Ryan, Mayer Chantal, and Rholing N Robert, “Automated Placenta Segmentation with a Convolutional Neural Network Weighted by Acoustic Shadow Detection,” in 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE , 2019, pp. 6718–6723.
C. Yu, S. Feng, J. Liu, and Y. Chen, “Prediction of hysterectomy in pernicious placenta previa by machine learning,” Asian Journal of Surgery, vol. 46, no. 5. Elsevier (Singapore) Pte Ltd, pp. 1957–1958, May 01, 2023. doi: 10.1016/j.asjsur.2022.11.068.
V. Romeo et al., “Machine learning analysis of MRI-derived texture features to predict placenta accreta spectrum in patients with placenta previa,” Magn Reson Imaging, vol. 64, pp. 71–76, Dec. 2019, doi: 10.1016/j.mri.2019.05.017.
P. D. W. Ayu, S. Hartati, A. Musdholifah, and D. S. Nurdiati, “Amniotic Fluids Classification Using Combination of Rules-Based and Random Forest Algorithm,” in Communications in Computer and Information Science, Springer Science and Business Media Deutschland GmbH, 2021, pp. 267–285. doi: 10.1007/978-981-16-7334-4_20.
P. D. W. Ayu, S. Hartati, A. Musdholifah, and D. S. Nurdiati, “Amniotic fluid classification based on volume and echogenicity using single deep pocket and texture feature,” ICIC Express Letters, vol. 15, no. 7, pp. 681–691, Jul. 2021, doi: 10.24507/icicel.15.07.681.
P. D. W. Ayu, S. Hartati, A. Musdholifah, and D. S. Nurdiati, “Amniotic fluid segmentation based on pixel classification using local window information and distance angle pixel,” in Applied Soft Computing, Elsevier Ltd, Aug. 2021. doi: 10.1016/j.asoc.2021.107196.
N. Orlando et al., “Umbilical cord blood: Current uses for transfusion and regenerative medicine,” Transfusion and Apheresis Science, vol. 59, no. 5. Elsevier Ltd, Oct. 01, 2020. doi: 10.1016/j.transci.2020.102952.
D. J. Wilke, J. P. Denier, T. Y. Khong, and T. W. Mattner, “Estimating umbilical cord flow resistance from measurements of the whole cord,” Placenta, vol. 103, pp. 180–187, Jan. 2021, doi: 10.1016/j.placenta.2020.09.066.
G. A. Pradipta, R. Wardoyo, A. Musdholifah, and I. N. H. Sanjaya, “Machine learning model for umbilical cord classification using combination coiling index and texture feature based on 2-D Doppler ultrasound images,” Health Informatics J, vol. 28, no. 1, Mar. 2022, doi: 10.1177/14604582221084211.
G. A. Pradipta, R. Wardoyo, A. Musdholifah, and I. N. H. Sanjaya, “Improving classifiaction performance of fetal umbilical cord using combination of SMOTE method and multiclassifier voting in imbalanced data and small dataset,” International Journal of Intelligent Engineering and Systems, vol. 13, no. 5, pp. 441–454, Oct. 2020, doi: 10.22266/ijies2020.1031.39.
A. Subasi, B. Kadasa, and E. Kremic, “Classification of the cardiotocogram data for anticipation of fetal risks using bagging ensemble classifier,” in Procedia Computer Science, Elsevier B.V., 2020, pp. 34–39. doi: 10.1016/j.procs.2020.02.248.
M. S. Iraji, “Prediction of fetal state from the cardiotocogram recordings using neural network models,” Artif Intell Med, vol. 96, pp. 33–44, May 2019, doi: 10.1016/j.artmed.2019.03.005.
K. Bache and M. Lichman, “UCI Machine Learning Repository,” UCI Machine Learning Repository [http://archive. ics. uci. edu/ml].
