SVM Optimization with Grid Search Cross Validation for Improving Accuracy of Schizophrenia Classification Based on EEG Signal
Abstract
The advantage of the Support Vector Machine (SVM) is that it can solve classification and regression problems both linearly and non-linearly. SVM also has high accuracy and a relatively low error rate. However, SVM also has weaknesses, namely the difficulty of determining optimal parameter values, even though setting exact parameter values affects the accuracy of SVM classification. Therefore, to overcome the weaknesses of SVM, optimizing and finding optimal parameter values is necessary. The aim of this research is SVM optimization to find optimal parameter values using the Grid Search Cross-Validation method to increase accuracy in schizophrenia classification. Experiments show that optimization parameters always find a nearly optimal combination of parameters within a specific range. The results of this study show that the level of accuracy obtained by SVM with the grid search cross-validation method in the schizophrenia classification increased by 9.5% with the best parameters, namely C = 1000, gamma = scale, and kernel = RBF, the best parameters were applied to the SVM algorithm and obtained an accuracy of 99.75%, previously without optimizing the accuracy reached 90.25%. The optimal parameters of the SVM obtained by the grid search cross-validation method with a high degree of accuracy can be used as a model to overcome the classification of schizophrenia.
Keywords
Full Text:
PDFReferences
WHO, "Schizophrenia," 2022. [Online]. Available: https://www.who.int/en/news-room/fact-sheets/detail/schizophrenia.
M.-H. Hiesh, Y.-Y. Andy Lam, C.-P. Shen, W. Chen, F.-S. Lin, H.-Y. Sung, J.-W. Lin, M.-J. Chiu, and F. Lai, Classification of Schizophrenia Using Genetic Algorithm-Support Vector Machine (GA-SVM). 2013.
F. Riyanda, I. Cholissodin, and Sutrisno, “Klasifikasi Gangguan Jiwa Skizofrenia Menggunakan Algoritme Decision Tree C5.0,” 2019.
W. F. Maramis, “Buku Ajar Ilmu Kedokteran Jiwa.” p. 783, 2009.
T. V. Rampisela and Z. Rustam, "Classification of Schizophrenia Data Using Support Vector Machine (SVM)," in Journal of Physics: Conference Series, 2018, vol. 1108, no. 1.
D. Kurniawaty, I. Cholissodin, and P. P. Adikara, “Klasifikasi Gangguan Jiwa Skizofrenia Menggunakan Algoritme Support Vector Machine (SVM),” 2018.
P. Sinha and P. Sinha, "Comparative Study of Chronic Kidney Disease Prediction using KNN and SVM," Int. J. Eng. Res., vol. V4, no. 12, pp. 608–612, 2015.
I. M. Elgohary, A. M. M Eissa, H. Mohamed, M. I. Elgohary, T. A. Alzohairy, A. M. Eissa, and sally Eldeghaidy, "An intelligent System for Diagnosing Schizophrenia and Bipolar Disorder based on MLNN and RBF," vol. 4, no. 2, pp. 117–123, 2016.
I. K. Laksono and M. E. Imah, “Klasifikasi Schizophrenia Berdasarkan Sinyal EEG Menggunakan Algoritma Support Vector Machine,” J. Ilm. Mat., vol. 7, no. 2, 2019.
M. Ahmad, N. Wahid, R. A. Hamid, S. Sadiq, A. Mehmood, and G. S. Choi, "Decision Level Fusion using Ensemble Classifier for Mental Disease Classification," pp. 1–23, 2020.
J. Sun, R. Cao, M. Zhou, W. Hussain, B. Wang, J. Xue, and J. Xiang, "A hybrid deep neural network for classification of schizophrenia using EEG Data," Sci. Rep., vol. 11, no. 1, pp. 1–17, 2021.
J. V. Tu, "Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes," J. Clin. Epidemiol., vol. 49, no. 11, pp. 1225–1231, 1996.
H. Harafani and R. S. Wahono, “Optimasi Parameter pada Support Vector Machine Berbasis Algoritma Genetika untuk Estimasi Kebakaran Hutan,” J. Intell. Syst., vol. 1, no. 2, 2015.
W. S. Noble, "What is a support vector machine?," Nat. Biotechnol., vol. 24, no. 12, pp. 1565–1567, 2006.
I. Ilhan and G. Tezel, "A genetic algorithm-support vector machine method with parameter optimization for selecting the tag SNPs," J. Biomed. Inform., vol. 46, no. 2, pp. 328–340, 2013.
A. Toha, P. Purwono, and W. Gata, “Model Prediksi Kualitas Udara dengan Support Vector Machines dengan Optimasi Hyperparameter Grid Search CV,” Bul. Ilm. Sarj. Tek. Elektro, vol. 4, no. 1, pp. 12–21, 2022.
Styawati, A. Nurkholis, Z. Abidin, and H. Sulistiani, “Optimasi Parameter Support Vector Machine Berbasis Algoritma Firefly Pada Data Opini Film,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 5, pp. 904–910, 2021.
I. G. N. E. Susena, M. T. Furqon, and R. C. Wihandika, “Optimasi Parameter Support Vector Machine ( SVM ) dengan Particle Swarm Optimization ( PSO ) Untuk Klasifikasi Pendonor Darah Dengan Dataset RFMTC,” S1 Univ. Brawijaya, vol. 2, no. 12, pp. 7278–7284, 2018.
A. Tharwat and A. E. Hassanien, "Optimizing support vector machine parameters using bat optimization algorithm," in Studies in Computational Intelligence, vol. 801, A. E. Hassanien, Ed. Cham: Springer International Publishing, 2019, pp. 351–374.
J. Zhou, Y. Qiu, S. Zhu, D. Jahed, and C. Li, "Engineering Applications of Artificial Intelligence Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate," Eng. Appl. Artif. Intell., vol. 97, no. September 2020, p. 104015, 2021.
J. W. Creswell, John W. Creswell’s Research Design 3rd Ed. 2009.
B. Roach, "EEG data from basic sensory task in Schizophrenia," 2019. [Online]. Available: https://www.kaggle.com/datasets/broach/button-tone-sz.
R. Feldman and J. Sanger, "The Text Mining Handbook: Advanced Approaches to Analyzing Unstructured Data," 2006.
W. K. Härdle and L. Simar, Applied Multivariate Statistical Analysis. 2019.
A. S. Nugroho, A. B. Witarto, and D. Handoko, “Support vector machine,” Mach. Learn. Methods Appl. to Brain Disord., pp. 1–5, 2003.
B. E. Boser, V. N. Vapnik, and I. M. Guyon, "Training Algorithm Margin for Optimal Classifiers," Perception, pp. 144–152, 1992.
G. S. K. Ranjan, A. Kumar Verma, and S. Radhika, "K-Nearest Neighbors and Grid Search CV Based Real Time Fault Monitoring System for Industries," 2019 IEEE 5th Int. Conf. Converg. Technol. I2CT 2019, pp. 9–13, 2019.
T. Yan, S. L. Shen, A. Zhou, and X. Chen, "Prediction of geological characteristics from shield operational parameters by integrating grid search and K-fold cross validation into stacking classification algorithm," J. Rock Mech. Geotech. Eng., vol. 14, no. 4, pp. 1292–1303, 2022.
K. R. Singh, K. P. Neethu, K. Madhurekaa, A. Harita, and P. Mohan, "Parallel SVM model for forest fire prediction," Soft Comput. Lett., vol. 3, no. June, p. 100014, 2021.
E. Hartini, "Classification of Missing Values Handling Method During Data Mining: Rview," vol. 21, no. 2, 2017.
DOI: https://doi.org/10.15408/jti.v17i1.37422 Abstract - 0 PDF - 0
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Masdar Desiawan, Achmad Solichin
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
3rd Floor, Dept. of Informatics, Faculty of Science and Technology, UIN Syarif Hidayatullah Jakarta
Jl. Ir. H. Juanda No.95, Cempaka Putih, Ciputat Timur.
Kota Tangerang Selatan, Banten 15412
Tlp/Fax: +62 21 74019 25/ +62 749 3315
Handphone: +62 8128947537
E-mail: jurnal-ti@apps.uinjkt.ac.id
Jurnal Teknik Informatika by Prodi Teknik Informatika Universitas Islam Negeri Syarif Hidayatullah Jakarta is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at http://journal.uinjkt.ac.id/index.php/ti.
JTI Visitor Counter: View JTI Stats