IMPROVING THE PERFORMANCE OF SUPPORT VECTOR MACHINE WITH FORWARD SELECTION FOR PREDICTION OF CHRONIC KIDNEY DISEASE
Abstract
Chronic kidney disease is a disorder that affects the kidneys and arises due to various factors. Chronic kidney disease, usually develops slowly and is chronic. For prevention and control, proper treatment is needed, so that detection of this disease can play a very important role. This study aims to determine the level of accuracy in predicting chronic kidney disease through SVM based on forward selection and to determine the performance of Feature Selection which is applied to the SVM method in solving problems in chronic kidney disease. This research was conducted an experiment on the SVM method using various kinds of kernels and it was seen that SVM with the dot kernel was 98.50% with AUC 1,000 which was superior to the polynominal kernel and RBF. However, when the experiment was carried out again by applying FS to SVM, it was found that SVM+FS with the RBF kernel outperformed the other kernels by 99.75% with AUC 1,000. So it can be concluded that the Forward Selection of SVM has succeeded in improving its performance, especially in this case, namely the prediction of chronic kidney disease
Downloads
References
[2] E. Febria Sari, “Waspada Ginjal Kronis,” Hermina Galaxy, 2021. [Online]. Available: https://herminahospitals.com/id/articles/waspada-gagal-ginjal-kronis.html. [Accessed: 04-Mar-2022].
[3] “Chronic Kidney Disease,” worldkidneyday.org, 2022. [Online]. Available: https://www.worldkidneyday.org/facts/chronic-kidney-disease/. [Accessed: 04-Mar-2022].
[4] Pusdatin, “Situasi Penyakit Ginjal Kronis,” pusdatin kemkes, Indonesia, pp. 1–4, 2017.
[5] I. Yulianti, R. Amegia Saputra, M. Sukrisno Mardiyanto, and A. Rahmawati, “Optimasi Akurasi Algoritma C4.5 Berbasis Particle Swarm Optimization dengan Teknik Bagging pada Prediksi Penyakit Ginjal Kronis Optimization of C4.5 Algorithm Based On Particle Swarm Optimization with Bagging Technique on Prediction of Chronic Kidney Dise,” Techno.COM, vol. 19, no. 4, pp. 411–421, 2020.
[6] Kemenkes RI, “Hasil Riset Kesehatan Dasar Tahun 2018,” 2018.
[7] N. A. Almansour et al., “Neural network and support vector machine for the prediction of chronic kidney disease: A comparative study,” Comput. Biol. Med., vol. 109, no. October 2018, pp. 101–111, 2019.
[8] Z. Chen, X. Zhang, and Z. Zhang, “Clinical risk assessment of patients with chronic kidney disease by using clinical data and multivariate models,” Int. Urol. Nephrol., vol. 48, no. 12, pp. 2069–2075, 2016.
[9] A. Y. Al-Hyari, A. M. Al-Taee, and M. A. Al-Taee, “Clinical decision support system for diagnosis and management of Chronic Renal Failure,” 2013 IEEE Jordan Conf. Appl. Electr. Eng. Comput. Technol. AEECT 2013, 2013.
[10] A. A. Serpen, “Diagnosis Rule Extraction from Patient Data for Chronic Kidney Disease Using Machine Learning,” Int. J. Biomed. Clin. Eng., vol. 5, no. 2, pp. 64–72, 2016.
[11] R. Ani, G. Sasi, U. R. Sankar, and O. S. Deepa, “Decision support system for diagnosis and prediction of chronic renal failure using random subspace classification,” 2016 Int. Conf. Adv. Comput. Commun. Informatics, ICACCI 2016, pp. 1287–1292, 2016.
[12] N. Tazin, S. Lecturer, S. A. Sabab, and M. T. Chowdhury, “Effective Classification and Feature Selection,” 2016 Int. Conf. Med. Eng. Heal. Informatics Technol., pp. 1–6, 2016.
[13] S. B. Akben, “Early Stage Chronic Kidney Disease Diagnosis by Applying Data Mining Methods to Urinalysis, Blood Analysis and Disease History,” Irbm, vol. 39, no. 5, pp. 353–358, 2018.
[14] H. Polat, H. Danaei Mehr, and A. Cetin, “Diagnosis of Chronic Kidney Disease Based on Support Vector Machine by Feature Selection Methods,” J. Med. Syst., vol. 41, no. 4, 2017.
[15] H.- Harafani, “Forward Selection pada Support Vector Machine untuk Memprediksi Kanker Payudara,” J. Infortech, vol. 1, no. 2, pp. 131–139, 2020.
[16] E. Purwaningsih, “Application of the Support Vector Machine and Neural Network Model Based on Particle Swarm Optimization for Breast Cancer Prediction,” SinkrOn, vol. 4, no. 1, p. 66, 2019.
[17] E. Purwaningsih, “Penerapan Particle Swarm Optimization pada Metode Neural Network untuk Perawatan Penyakit Kutil melalui Immunotherapy,” J. Sist. dan Teknol. Inf., vol. 8, no. 2, p. 207, 2020.
[18] W. Li and Z. Liu, “A method of SVM with normalization in intrusion detection,” Procedia Environ. Sci., vol. 11, no. PART A, pp. 256–262, 2011.
[19] J. Han, J; Kamber, M; & Pei, Data Mining Concepts and Techniques. In Data Mining. 2012.
[20] E. Purwaningsih, “Seleksi Mobil Berdasarkan Fitur dengan Komparasi Metode Klasifikasi Neural Network, Support Vector Machine, dan Algoritma C4.5,” J. Pilar Nusa Mandiri, vol. XII, no. 2, pp. 153–160, 2016.
Copyright (c) 2022 Esty Purwaningsih
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
-a.jpg)
-b.jpg)
