Hybrid Support Vector Machine to Preterm Birth Prediction

https://doi.org/10.22146/ijeis.35817

Noviyanti Santoso(1*), Sri Pingit Wulandari(2)

(1) Institut Teknologi Sepuluh Nopember
(2) Institut Teknologi Sepuluh Nopember
(*) Corresponding Author

Abstract


Preterm birth is one of the major contributors to perinatal and neonatal mortality. This issue became important in health research area especially human reproduction both in developed and developing country. In 2015 Indonesia rank fifth as the country with the highest number of premature babies in the world. The ability to reduce the number of preterm birth is to reduce risk factors associated with it. This research will be made the prediction model of preterm birth using hybrid multivariate adaptive regression splines (MARS) and Support Vector Machine (SVM). MARS used to select the attributes which suspected to affect premature babies. The result of this research is prediction model based on hybrid MARS-SVM obtains better performance than the other models

Keywords


preterm birth prediction; support vector machine; MARS; hybrid; classification

Full Text:

PDF


References

[1]            H. Blencowe, S. Cousens, D. Chou, M. Oestergaard, L. Say, A. B. Moller, M. Kinney and J. Lawn, “Born Too Soon: The global epidemiology of 15 million preterm births,” Reproductive Health, vol. 10 (Suppl 1): S2, 2013. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3828585/. [Accessed: 14-May-2018]

[2]            Dinas kesehatan Provinsi Jatim 2014. Buku Profil Kesehatan Jatim 2014. Jawa Timur.

[3]            D. Sulistiarini and S. M. Berliana, “Faktor-Faktor Yang Memengaruhi Kelahiran Prematur Di Indonesia: Analisis Data Riskesdas 2013,” E-Journal WIDYA Kesehatan Dan Lingkungan, vol. 1, no. 2, pp. 109-115, March 2016. [Online]. Available: http://e-journal.jurwidyakop3.com/index.php/kes-ling/article/view/242. [Accessed: 12-April-2018]

[4]            A. A. H. Asl, S. Safari, and M. P. Hamrah,  “Epidemiology and Related Risk Factors of Preterm Labor as an obstetrics emergency”, An Academic Emergency Medicine Journal, vol 5, no. 1, Jan. 2017. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5325899/. [Accessed: 20-March-2018]

[5]            Y. P. Zhang, X. H. Liu, S. H. Gao, J. M. Wang, Y. S. Gu, J. Y. Zhang, X. Zhou, and Q. X. Li, “Risk Factors for Preterm Birth in Five Maternal and Child Health Hospitals in Beijing”, PLoS ONE, vol 7, no. 12, Dec. 2012. [Online]. Available: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0052780. [Accessed: 20-March-2018]

[6]            T. B. Temu, G. Masenga, J. Obure, D. Mosha, and M. J. Mahande, “Maternal and obstetric risk factors associated with preterm delivery at a referral hospital in northern-eastern Tanzania,” Asian Pacific Journal of Reproduction, Vol. 5, Issue 5, pp. 365-370, Sept 2016. [Online]. Available:

https://www.sciencedirect.com/science/article/pii/S2305050016300768. [Accessed: 20-March-2018]

[7]            J. H. Friedman, “Multivariate adaptive regression splines (with discussion)”, Annals of Statistics, vol. 19, pp. 1–141, 1991.

[8]            Suroto, B. W. Otok, Suharto, A. Wibowo, “Multivariate Adaptive Regression Spline for Prediction of Hypertension Cases the Measurement of Blood Pressure in Indonesia”. J.Appl. Environ. Biol. Sci., vol. 7, no. 5, pp. 41-46, 2017. [Online]. Available: https://www.textroad.com/pdf/JAEBS/J.%20Appl.%20Environ.%20Biol.%20Sci.,%207(5)41-46,%202017.pdf. [Accessed: 4-March-2018]

[9]            S. W. Purnami, S. Andari, and Y. D. Pertiwi, “High-Dimensional Data Classification Based on Smooth Support Vector Machines”, Procedia Computer Science, vol. 72, pp. 477 – 484, 2015. [Online]. Available:

https://www.sciencedirect.com/science/article/pii/S1877050915035905. [Accessed: 4-March-2018]

[10]        D. Yao, J. Yang, and X. Zhan, “A Novel Method for Disease Prediction: Hybrid of Random Forest and Multivariate Adaptive Regression Splines”, Journal of Computers, vol. 8, no. 1, 2013. [Online]. Available:

http://www.jcomputers.us/index.php?m=content&c=index&a=show&catid=50&id=523. [Accessed: 20-March-2018]

[11]        Nidhomuddin and B. W. Otok, “Random Forest Dan Multivariate Adaptive Regression Spline (MARS) Binary Response Untuk Klasifikasi Penderita HIV/AIDS Di Surabaya”, Jurnal Statistika Universitas Muhammadiyah Semarang, vol. 3, no.1, 2015. [Online]. Available: https://jurnal.unimus.ac.id/index.php/statistik/article/view/1439. [Accessed: 10-April-2018]

[12]        V. N. Vapnik, The Nature of Statistical Learning Theory, New York: Springer, 1995.

[13]        S. Li and S. Oh, “Improving feature selection performance using pairwise pre-evaluation,” BioMed Central Bioinformatics, vol. 17, pp. 312, Aug 2016. [Online]. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4992252/. [Accessed: 14-May-2018]

[14]        D. S. Kumar and S. Sukanya, “Feature Selection Using Multivariate Adaptive Regression Splines,” International Journal of Research and Reviews in Applied Sciences And Engineering (IJRRASE), vol. 8, no.1, pp. 17-24, 2016. [Online]. Available: http://www.ijcns.com/pdf/ijrrasevol8no11016-4.pdf

[15]        J. H. Friedman and B. W. Silverman, “Flexible Parsimony Smoothing and Additive Modelling”, Technometrics, vol. 31, 1989.

[16]        N. Santoso, W. Wibowo, “Financial Distress Prediction using Linear Discriminant Analysis and Support Vector Machine,” in AIP Conf. Series: Journal of Physics: Conf. Series, 2018, vol. 979, pp. 012089. [Online]. Available: http://iopscience.iop.org/article/10.1088/1742-6596/979/1/012089.

[17]        C.C. Chang and C. J. Lin, “LIBSVM: A library for support vector machine,” 2013. Available: https://www.csie.ntu.edu.tw/~cjlin//papers/libsvm.pdf



DOI: https://doi.org/10.22146/ijeis.35817

Article Metrics

Abstract views : 905 | views : 801

Refbacks

  • There are currently no refbacks.




Copyright (c) 2018 IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.



Copyright of :
IJEIS (Indonesian Journal of Electronics and Instrumentations Systems)
ISSN 2088-3714 (print); ISSN 2460-7681 (online)
is a scientific journal the results of Electronics
and Instrumentations Systems
A publication of IndoCEISS.
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Fax: +62274 555133
email:ijeis.mipa@ugm.ac.id | http://jurnal.ugm.ac.id/ijeis



View My Stats1
View My Stats2