Support Vector Machine Parameter Optimization to Improve Liver Disease Estimation with Genetic Algorithm

Main Article Content

Hani Harafani
Corresponding Author:
Hani Harafani | haniharafani@gmail.com

Copyright (C):
Hani Harafani

Abstract

Liver disease is an important public health problem. Over the past few decades, machine learning has developed rapidly and it has been introduced for application in medical-related. In this study we propose Support Vector Machine optimization parameter with genetic algorithm to get a higher performance of Root Mean Square Error value of SVM in order to estimate the liver disorder. The experiment was carried out in three stages, the first step was to try the three SVM kernels with different combination of parameters manually, The second step was to try some combination of range parameters in the genetic algorithm to find the optimal value in the SVM kernel. The third step is comparing the results of the GA-SVM experiment with other regression methods. The results prove that GA has an influence on improving the performance of GA-SVM which has the lowest RMSE value compared to another regression models.

Keyword: SVM;GA;Liver;Parameter;Estimation

Downloads

Download data is not yet available.

Article Details

How to Cite
HARAFANI, Hani. Support Vector Machine Parameter Optimization to Improve Liver Disease Estimation with Genetic Algorithm. SinkrOn, [S.l.], v. 4, n. 2, p. 106-114, apr. 2020. ISSN 2541-2019. Available at: <https://jurnal.polgan.ac.id/index.php/sinkron/article/view/10524>. Date accessed: 09 july 2020. doi: https://doi.org/10.33395/sinkron.v4i2.10524.
Section
Articles
* Abstract viewed = 0 times PDF downloaded = 0 times *

References

Beeching, N., & Dassanayake, A. (2019). Tropical liver disease. Medicine (United Kingdom), 47(11), 758–763. https://doi.org/10.1016/j.mpmed.2019.08.001

Bhuvaneswari, P., & Therese, A. B. (2015). Detection of Cancer in Lung With K-NN Classification Using Genetic Algorithm. Procedia Materials Science, 10(Cnt 2014), 433–440.

Chen, R., Liang, C.-Y., Hong, W.-C., & Gu, D.-X. (2015). Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm. Applied Soft Computing, 26, 435–443. https://doi.org/10.1016/j.asoc.2014.10.022

Goyal, R., Chandra, P., & Singh, Y. (2014). Suitability of KNN Regression in the Development of Interaction Based Software Fault Prediction Models. IERI Procedia, 6, 15–21. Retrieved from http://dx.doi.org/10.1016/j.ieri.2014.03.004

Harafani, H., & Maulana, A. (2019). Penerapan Algoritma Genetika pada Support Vector Machine Sebagai Pengoptimasi Parameter untuk Memprediksi Kesuburan. V(1), 51–59.

Harafani, H., & Wahono, R. S. (2015). Optimasi Parameter pada Support Vector Machine Berbasis Algoritma Genetika untuk Estimasi Kebakaran Hutan. Journal of Intelligent Systems, 1(2).

Kavousi-Fard, A., Samet, H., & Marzbani, F. (2014). A new hybrid Modified Firefly Algorithm and Support Vector Regression model for accurate Short Term Load Forecasting. Expert Systems with Applications, 41(13), 6047–6056. https://doi.org/10.1016/j.eswa.2014.03.053

Kelly, D. A. (2019). Liver and biliary disease in childhood. Medicine (United Kingdom), 47(12), 782–786. https://doi.org/10.1016/j.mpmed.2019.09.011

Lantz, B. (2015). Machine Learning with R Learn.

Liang, H., Zou, J., Li, Z., Junaid, M., & Lu, Y. (2019). Dynamic evaluation of drilling leakage risk based on fuzzy theory and PSO-SVR algorithm. Future Generation Computer Systems, 95, 454–466. https://doi.org/10.1016/j.future.2018.12.068

Lira, M. A. T., Da Silva, E. M., Alves, J. M. B., & Veras, G. V. O. (2014). Estimation of wind resources in the coast of Ceará, Brazil, using the linear regression theory. Renewable and Sustainable Energy Reviews, 39, 509–529. Retrieved from http://dx.doi.org/10.1016/j.rser.2014.07.097

Liu, H., Tian, H., Chen, C., & Li, Y. (2013). Electrical Power and Energy Systems An experimental investigation of two Wavelet-MLP hybrid frameworks for wind speed prediction using GA and PSO optimization. International Journal of Electrical Power & Energy Systems, 52, 161–173. https://doi.org/10.1016/j.ijepes.2013.03.034

Maimon, O., & Rokach, L. (2010). Data Mining and Knowledge Discovery Handbook.

McDermott, J., & Forsyth, R. S. (2016). Diagnosing a disorder in a classification benchmark. Pattern Recognition Letters, 73, 41–43. https://doi.org/10.1016/j.patrec.2016.01.004

Purwaningsih, E. (2019). Application of the Support Vector Machine and Neural Network Model Based on Particle Swarm Optimization for Breast Cancer Prediction. SinkrOn, 4(1), 66. https://doi.org/10.33395/sinkron.v4i1.10195

Raghavendra. N, S., & Deka, P. C. (2014). Support vector machine applications in the field of hydrology: A review. Applied Soft Computing, 19, 372–386. Retrieved from http://linkinghub.elsevier.com/retrieve/pii/S1568494614000611

Raikar, R. V., Wang, C. Y., Shih, H. P., & Hong, J. H. (2016). Prediction of contraction scour using ANN and GA. Flow Measurement and Instrumentation, 50, 26–34. https://doi.org/10.1016/j.flowmeasinst.2016.06.006

Venkata Ramana, B., Babu, M. S. P., & Venkateswarlu, N. . (2011). A Critical Study of Selected Classification Algorithms for Liver Disease Diagnosis. International Journal of Database Management Systems, 3(2), 101–114. https://doi.org/10.5121/ijdms.2011.3207

Wang, X., Wen, J., Zhang, Y., & Wang, Y. (2014). Optik Real estate price forecasting based on SVM optimized by PSO. Optik - International Journal for Light and Electron Optics, 125(3), 1439–1443. Retrieved from http://dx.doi.org/10.1016/j.ijleo.2013.09.017

Yao, Z., Li, J., Guan, Z., Ye, Y., & Chen, Y. (2020). Liver disease screening based on densely connected deep neural networks. Neural Networks, 123, 299–304. https://doi.org/10.1016/j.neunet.2019.11.005

Zaghloul, M. S., Hamza, R. A., Iorhemen, O. T., & Tay, J. H. (2020). Comparison of adaptive neuro-fuzzy inference systems (ANFIS) and support vector regression (SVR) for data-driven modelling of aerobic granular sludge reactors. Biochemical Pharmacology, (November 2019), 103742. https://doi.org/10.1016/j.jece.2020.103742

Zhang, L., & Wang, N. (2018). Application of coRNA-GA based RBF-NN to model proton exchange membrane fuel cells. International Journal of Hydrogen Energy, 43(1), 329–340. https://doi.org/10.1016/j.ijhydene.2017.11.027