Comparison of Kernel Function on Support Vector Machine in Classification of Childbirth
AbstractThe maternal mortality rate during childbirth can be reduced through the efforts of the medical team in determining the childbirth process that must be undertaken immediately. Machine learning in terms of classifying childbirth can be a solution for the medical team in determining the childbirth process. One of the classification methods that can be used is the Support Vector Machine (SVM) method which is able to determine a hyperplane that will form a good decision boundary so that it is able to classify data appropriately. In SVM, there is a kernel function that is useful for solving non-linear classification cases by transforming data to a higher dimension. In this study, four kernel functions will be used; Linear, Radial Basis Function (RBF), Polynomial, and Sigmoid in the classification process of childbirth in order to determine the kernel function that is capable of producing the highest accuracy value. Based on research that has been done, it is obtained that the accuracy value generated by SVM with linear kernel functions is higher than the other kernel functions.
S. Liu et al., “Maternal mortality and severe morbidity associated with low-risk planned cesarean delivery versus planned vaginal delivery at term.,” CMAJ, vol. 176, no. 4, pp. 455–60, Feb. 2007.
M. Wagner, “Choosing caesarean section.,” Lancet (London, England), vol. 356, no. 9242, pp. 1677–80, Nov. 2000.
A. Amin, Muhammad & Ali, “Performance Evaluation of Supervised Machine Learning Classifiers for Predicting Healthcare Operational Decisions,” 2018.
Q. Li, Q. Meng, J. Cai, H. Yoshino, and A. Mochida, “Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks,” Energy Convers. Manag., vol. 50, no. 1, pp. 90–96, Jan. 2009.
Y. Shao and R. S. Lunetta, “Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points,” ISPRS J. Photogramm. Remote Sens., vol. 70, pp. 78–87, Jun. 2012.
E. Byvatov, U. Fechner, J. Sadowski, and G. Schneider, “Comparison of Support Vector Machine and Artificial Neural Network Systems for Drug/Nondrug Classification,” J. Chem. Inf. Comput. Sci., vol. 43, no. 6, pp. 1882–1889, Nov. 2003.
Y. I. A. Rejani and S. T. Selvi, “Early Detection of Breast Cancer using SVM Classifier Technique,” Dec. 2009.
D. Novitasari, “Klasifikasi Alzheimer dan Non Alzheimer Menggunakan Fuzzy C-Mean, Gray Level Co-Occurence Matrix dan Support Vector Machine”, mantik, vol. 4, no. 2, pp. 83-89, Oct. 2018.
S. Pahwa and D. Sinwar, “Comparison Of Various Kernels Of Support Vector Machine,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 3, no. VII, pp. 532–536, 2015.
John_Shawe-Taylor_&_Nello_Christianini, Kernel Methods For Pattern Analysis, vol. 111, no. 479. New York: Cambridge university Press, 1965.
C.-W. Hsu, C.-C. Chang, and C.-J. Lin, “A Practical Guide to Support Vector Classification,” 2003.
B. E. Boser, I. M. Guyon, and V. N. Vapnik, “Training algorithm for optimal margin classifiers,” Proc. Fifth Annu. ACM Work. Comput. Learn. Theory, pp. 144–152, 1992.
Christopher M Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics). Heidelberg: Springer-Verlag Berlin, 2006.
“1.4. Support Vector Machines — scikit-learn 0.21.3 documentation.” [Online]. Available: https://scikit-learn.org/stable/modules/svm.html#svm-classification. [Accessed: 29-Aug-2019].
F. Pedregosa Fabianpedregosa et al., “Scikit-learn: Machine Learning in Python Gaël Varoquaux Bertrand Thirion Vincent Dubourg Alexandre Passos Pedregosa, Varoquaux, Gramfort Et Al. Matthieu Perrot,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011.
C. Gold and P. Sollich, “Model selection for support vector machine classification,” Neurocomputing, vol. 55, no. 1–2, pp. 221–249, 2003.
J. Bergstra and Y. Bengio, “Random Search for Hyper-Parameter Optimization,” J. Mach. Learn. Res., vol. 13, no. Feb, pp. 281–305, 2012.
F. Gorunescu, Dana Mining: Concepts, Models, and Techniques. New York: Springer US, 2011.
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