Application of the Support Vector Machine and Neural Network Model Based on Particle Swarm Optimization for Breast Cancer Prediction

Main Article Content

Esty Purwaningsih

Abstract

There are several studies in the medical field that classify data to diagnose and analyze decisions. To predict breast cancer, this study compares two methods, the Support Vector Machine method and the Neural Network method based on Particle Swarm Optimization (PSO) which is intended to determine the highest accuracy value in the Coimbra dataset data. To implement the Support Vector Machine and Neural Network method based on PSO, RapidMiner software is used. Then the application results are compared using Confusion Matrix and ROC Curve. Based on the accuracy of the two models, it is known that the PSO-based Neural Network model has a higher accuracy value of 84.55% than the results of the PSO-based Vector Support Machine with an accuracy value of 80.08%. The calculation results, the accuracy of the AUC performance obtained by the results of the study are, the two methods are PSO-based Neural Network with AUC value of 0.885 and PSO-based Support Vector Machine with a value of 0.819 included in the category of Good Classification.

Downloads

Download data is not yet available.

Article Details

How to Cite
PURWANINGSIH, Esty. Application of the Support Vector Machine and Neural Network Model Based on Particle Swarm Optimization for Breast Cancer Prediction. SinkrOn, [S.l.], v. 4, n. 1, p. 66-73, sep. 2019. ISSN 2541-2019. Available at: <https://jurnal.polgan.ac.id/index.php/sinkron/article/view/10195>. Date accessed: 21 nov. 2019. doi: https://doi.org/10.33395/sinkron.v4i1.10195.
Section
Articles
**************** Abstract viewed = 54 times ****************

References

Asria, Hiba, H. M., Moatassime, H. Al, C, & Noeld, T. (2016). Using Machine Learning Algorithms for Breast Cancer Risk Prediction and Diagnosis. Procedia Computer Science, 83, 1064–1069. Retrieved from https://ac.els-cdn.com/S1877050916302575/1-s2.0-S1877050916302575-main.pdf?_tid=e2d06037-c161-4e91-a842-8ce3db6bd633&acdnat=1552990694_8bc70a70ef15b5bad610be0e54cc9e58

Biro Komunikasi dan Pelayanan Masyarakat, K. K. R. (2019). Kementerian Kesehatan Republik Indonesia. Retrieved September 3, 2019, from Biro Komunikasi dan Pelayanan Masyarakat, Kementerian Kesehatan RI website: http://www.depkes.go.id/article/view/19020100003/hari-kanker-sedunia-2019.html

Cao, J., Cui, H., Shi, H., & Jiao, L. (2016). Big data: A parallel particle swarm optimization-back-propagation neural network algorithm based on MapReduce. PLoS ONE, 11(6), 1–17. https://doi.org/10.1371/journal.pone.0157551

Harafani, H. (2015). Optimasi Parameter pada Support Vector Machine Berbasis Algoritma Genetika untuk Estimasi Kebakaran Hutan. Journal of Intelligent Systems, 1(2), 82–90.

Maimon, Oded&Rokach, L. (2010). Data Mining and Knowledge Discovey Handbook. New York: Springer.

Novianti, F. A., & Purnami, S. W. (2012). Analisis Diagnosis Pasien Kanker Payudara Menggunakan Regresi Logistik dan Support Vector Machine (SVM) Berdasarkan Hasil Mamografi. Jurnal Sains Dan Seni ITS, 1(1), D147--D152.

Patrício, M., Pereira, J., Crisóstomo, J., Matafome, P., Gomes, M., Seiça, R., & Caramelo, F. (2018). Using Resistin , glucose , age and BMI to predict the presence of breast cancer. BMC Cancer, DOI 10.118, 1–8. https://doi.org/10.1186/s12885-017-3877-1

Ridwansyah, & Purwaningsih, E. (2018). PARTICLE SWARM OPTIMIZATION UNTUK MENINGKATKAN AKURASI PREDIKSI PEMASARAN BANK. Jurnal PILAR Nusa Mandiri, 14(1), 83–88.

Satapathy, S. C., Chittineni, S., Mohan Krishna, S., Murthy, J. V. R., & Prasad Reddy, P. V. G. D. (2012). Kalman particle swarm optimized polynomials for data classification. Applied Mathematical Modelling, 36(1), 115–126. https://doi.org/10.1016/J.APM.2011.05.033

Vercellis, C. (2009). Business Intelligent: Data Mining and Optimizzation for Decision Making. Southern Gate, Chichester, West Sussex, United Kingdom: John Wiley & Sons Ltd.

Vieira, S. M., Mendonça, L. F., Farinha, G. J., & Sousa, J. M. C. (2013). Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patients. Applied Soft Computing, 13(8), 3494–3504. https://doi.org/10.1016/J.ASOC.2013.03.021

Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining : Practical Machine Learning and Tools. Burlington: Morgan Kaufmann Publisher.

Zamani, A. M., & Amaliah, B. (2012). Implementasi Algoritma Genetika pada Struktur Backpropagation Neural Network untuk Klasifikasi Kanker Payudara. Jurnal Teknik POMITS, 1(1), 1–6. https://doi.org/10.12962/j23373539.v1i1.638