SVM Optimization Based on PSO and AdaBoost to Increasing Accuracy of CKD Diagnosis

Main Article Content

Amanah Febrian Indriani Much Aziz Muslim

Abstract

Classification is data mining techniques which used for the purposes of diagnosis in the medical field as measured by the high accuracy produced. The accuracy of classification algorithm is influenced by the use of features and dimensions in dataset. In this study, Chronic Kidney Disease (CKD) dataset was used where the data is one of the high dimension datasets. Support Vector Machine (SVM) algorithm is used because its ability to handle high-dimensional data. In the dataset, it consists of 24 attributes and 1 class which if all are used results accuracy of classification will be diminished. Method for selecting features with Particle Swarm Optimization (PSO) is applied to reduce redundant features and produce optimal features. In addition, ensemble AdaBoost also applied in this research to increase performance of entirety classification algorithm. The results showed that the optimization of SVM algorithm by using PSO as a selection and ensemble feature of AdaBoost with an average of selected features of 18 features could increase the accuracy of 36.20% to 99.50% in the diagnosis of CKD compared to the SVM algorithm without optimization only resulting in accuracy 63.30%. This research can be used as a reference for further research in focusing on the preprocessing stage.

Downloads

Download data is not yet available.

Article Details

How to Cite
INDRIANI, Amanah Febrian; MUSLIM, Much Aziz. SVM Optimization Based on PSO and AdaBoost to Increasing Accuracy of CKD Diagnosis. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi, [S.l.], p. 119-127, aug. 2019. ISSN 2541-5832. Available at: <https://ojs.unud.ac.id/index.php/lontar/article/view/48406>. Date accessed: 06 dec. 2019. doi: https://doi.org/10.24843/LKJITI.2019.v10.i02.p06.
Section
Articles

References

[1] M. H. Elhebir and A. Abraham, "A Novel Ensemble Approach to Enhance the Performance of Web Server Logs Classification," International Journal of Computer Information Systems and Industrial Management Applications, vol. 7, pp. 189-195, 2015.
[2] G. A. Afzali and S. Mohammadi, "Privacy Preserving Big Data Mining: Association Rule Hiding," Journal of Information System and Telecomunication, vol. 4, no. 2, pp. 70-77, 2016.
[3] H. Hamidi and A. Daraei, "Analysis and Evaluation of Techniques for Myocardial Infraction Based on Genetic Algorithm and Weight by SVM," Journal of Information System and Telecommunication, vol. 4, no. 2, pp. 85-91, 2016.
[4] M. A. Muslim, E. Sugiharti, B. Prasetiyo and S. Alimah, "Penerapan Dizcrretization dan Teknik Bagging UNtuk Meningkatkan Akurasi Klasifikasi Berbasis Enseble pada Algoritma C4.5 dalam Mendiagnosa Diabetes," LONTAR KOMPUTER: Jurnal Ilmiah Teknologi Informasi, vol. 8, no. 2, pp. 135-143, 2017.
[5] L. J. Rubini and P. Eswaran, "Generating Comparative Analysis of Early Stage Prediction of Chronic Kidney Disease," International Journal of Modern Engineering Research (IJMER), vol. 5, no. 7, pp. 49-55, 2015.
[6] I. Fadilla, P. P. Adikara and R. S. Perdana, "Klasifikasi Penyakit Chronic Kidney Disease (CKD) Dengan Menggunakan Metode Extreme Learning Machine (ELM)," Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer e-ISSN 2548:964X, vol. 2, no. 10, pp. 3397-3405, 2018.
[7] W. Abedalkhader and N. Abdulrahman, "Missing Data Classification of Chronic Kidney Disease," International Journal of Data Mining & Knowledge Management Process (IJDKP), vol. 7, no. 5, pp. 55-61, 2017.
[8] A. Widodo and S. Handoyo, "The Classification Performance using Logistic regression and Support Vector Machine (SVM)," Journal of Theoritical and Applied Information Technology, vol. 95, no. 19, pp. 5184-5193, 2017.
[9] A. Jamal, A. Handayani, A. A. Septiandri, E. Ripmiatin and Y. Effendi, "Dimensionality Reduction using PCA and K-Means Clustering for Breast Cancer Prediction," LONTAR KOMPUTER: Jurnal Ilmiah Teknologi Informasi, vol. 9, no. 3, pp. 192-201, 2018.
[10] F. S. Jumeilah, "Penerapan Support Vector Machine (SVM) untuk Pengkategorian Penelitian," JURNAL RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 1, no. 1, pp. 19-25, 2017.
[11] M. Mohammadpour, M. Ghorbanian and S. Mozaffari, "AdaBoost Performance Improvement Using PSO Algorithm," in 2016 Eight International Conference on Information and Knowledge Technology (IKT), Iran, 2016.
[12] R. Wang, "AdaBoost for Feature Selection, Classification and Its Relation with SVM, A Review," Physics Procedia, vol. 25, pp.800-807, 2012.
[13] D. Panday, R. C. de Amorim and P. Lane, "Feature weighting as a tool for unsupervised feature selection," Information Processing Letters, vol. 129, pp. 44-52, 2018.
[14] M. H. Aghdam and S. Heidari, "Feature Seletion Using Particel Swarm Optimization in Text Categorization," Journal of Artificial Intelligence and Soft Computing Research, vol. 5, no. 4, pp. 231-238, 2015.
[15] F. Mar'i and A. A. Supianto, "Clustering Credit Card Holder Berdasarkan Pembayaran Tagihan Menggunakan Improved K-Means dengan Particle Swarm Optimization," Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 5, no. 6, pp. 737-744, 2018.
[16] M. A. Muslim, A. Nurzahputra and B. Prasetiyo, "Improving Accuracy of C4.5 Algorithm Using Split Feature Reduction Model and Bagging Ensemble for Credit Card Risk Prediction," in International Conference on Information and Communications Technology (ICOIACT), Yogyakarta, 2018.
[17] F. Ardjani, K. Sadouni and M. Benyettou, "Optimization of SVM MultiClass by Particle Swarm (PSO-SVM)," in International Workshop on Database Technology and Applications, China, 2010.
[18] L. Y. Chuang, C. H. Ke and C. H. Yang, "A Hybrid Both Filter and Wrapper Feature Selection Method for Microarray Classification," in Internation MiltiConference of Engineers and Computer Scientist 2008, Hong Kong, 2008.
[19] S. Gunasundari, S. Janakiraman and S. Meenambal, "Multiswarm Heterogeneous Binary PSO using Win-Win approach for improved Feature Selection in Liver and Kidney disease Diagnosis," Computerized Medical Imaging and Graphics, vol. 70, pp. 135-154, 2018.
[20] Z. H. Zhou, Ensemble Methods: Foundations and Algorithms, Chapman and Hall: CRC, 2012.
[21] J. Han, M. Kamber and J. Pei, Data Mining: Concepts and Techniques, Waltham, MA: Morgan Kaufman Publisher (Elsivier), 2012.
[22] A. Nurzahputra and M. A. Muslim, "Peningkatan Akurasi Pada Algoritma C4.5 Menggunakan AdaBoost untuk Meminimalkan Resiko Kredit," in Prosiding SNATIF, Kudus, 2017, pp. 243-247.
[23] E. Listiana and M. A. Muslim, "Penerapan Adaboost Untuk Klasifikasi Support Vector Machine Guna Meningkatkan Akurasi Pada Diagnosis Chronic Kidney Disease," in Prosiding SNATIF, Kudus, 2017, pp. 875-881.
[24] Y. Wang and X. Li, "Improvement of RBF Neural Network by AdaBoost Algorithm Combined with PSO," Telkomnika, vol. 14, no. 3A, pp. 56, 2016.
[25] S. Vijayarani and S. Dhayanand, "Data Mining Classification Algorithm for Kidney Disease Prediction," International Journal on Cybernetics & Informatics (IJCI), vol. 4, no. 4, pp. 13-25, 2015.
[26] A. Subasi, "Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders," Computers in biology and medicine, vol. 43, no. 5, pp. 576-586, 2013.
[27] U. Bhosle and J. Deshmukh, "Mammogram classification using AdaBoost with RBFSVM and Hybrid KNN–RBFSVM as base estimator by adaptively adjusting γ and C value," International Journal Information and Technology, pp. 1-8, 2018.