Optimization of support vector machine using information gain and adaboost to improve accuracy of chronic kidney disease diagnosis
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Abstract
Today's database is growing very rapidly, especially in the field of health. The data if not processed properly then it will be a pile of data that is not useful, so the need for data mining process to process the data. One method of data mining used to predict a decision in any case is classification, where in the classification method there is a support vector machine algorithm that can be used to diagnose chronic kidney disease. The purpose of this study is to determine the level of accuracy of the application of information gain and AdaBoost on the support vector machine algorithm in diagnosing chronic kidney disease. The use of information gain is to select the attributes that are not relevant while AdaBoost is used as an ensemble method commonly known as the method of classifier combination. In this study the data used are chronic kidney disease (CKD) dataset obtained from UCI repository of machine learning. The result of experiment using MATLAB applying information gain and AdaBoost on vector machine support algorithm with k-fold cross validation default k = 10 shows an accuracy increase of 0.50% with the exposure of the result as follows, the support vector machine algorithm has accuracy of 99.25 %, if by applying AdaBoost on the support vector machine has an accuracy of 99.50%, whereas if applying AdaBoost and information gain on the support vector machine has an accuracy of 99.75%.
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References
E. Sugiharti, S. Firmansyah, and F. R. Devi, “Predictive evaluation of performance of computer science students of unnes using data mining based on naÏve bayes classifier (NBC) algorithm,” J. Theor. Appl. Inf. Technol., vol. 95, pp. 902–911, Feb. 2017.
M. Muslim, I. Kurniawati, and E. Sugiharti, “Expert system diagnosis chronic kidney disease based on mamdani fuzzy inference system,” vol. 78, pp. 70–75, Aug. 2015.
M. R. Hidayah, I. Akhlis, and E. Sugiharti, “Recognition Number of The Vehicle Plate Using Otsu Method and K-Nearest Neighbour Classification,” Sci. J. Informatics, vol. 4, no. 1, pp. 66–75, May 2017, doi: 10.15294/sji.v4i1.9503.
A. Nurdina and A. B. I. Puspita, “Naive Bayes and KNN for Airline Passenger Satisfaction Classification: Comparative Analysis,” J. Inf. Syst. Explor. Res., vol. 1, no. 2, Jul. 2023, doi: 10.52465/joiser.v1i2.167.
K. P. Wijaya and M. A. Muslim, “Peningkatan Akurasi pada Algoritma Support Vector Machine dengan Penerapan Information Gain untuk Mendiagnosa Chronic Kidney Disease,” in Seminar Nasional Ilmu Komputer, Semarang: Universitas Negeri Semarang, 2016, pp. 22–27.
I. A. Ashari, M. A. Muslim, and A. Alamsyah, “Comparison Performance of Genetic Algorithm and Ant Colony Optimization in Course Scheduling Optimizing,” Sci. J. Informatics, vol. 3, no. 2, pp. 149–158, Nov. 2016, doi: 10.15294/sji.v3i2.7911.
A. Nurzahputra and M. A. Muslim, “Peningkatan Akurasi Pada Algoritma C4.5 Menggunakan Adaboost Untuk Meminimalkan Resiko Kredit,” Seminar Nasional Teknologi dan Informatika 2017. Indonesia, 2017.
E. Sugiharti and M. Muslim, “On-line clustering of lecturers performance of computer science department of semarang state university using K-MeansAlgorithm,” vol. 83, pp. 64–71, Jan. 2016.
J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques. 2012.
D. Aprilianto, “SVM Optimization with Correlation Feature Selection Based Binary Particle Swarm Optimization for Diagnosis of Chronic Kidney Disease,” J. Soft Comput. Explor., vol. 1, no. 1, Sep. 2020, doi: 10.52465/joscex.v1i1.1.
V. S and D. S, “Data Mining Classification Algorithms for Kidney Disease Prediction,” Int. J. Cybern. Informatics, vol. 4, no. 4, pp. 13–25, Aug. 2015, doi: 10.5121/ijci.2015.4402.
X. Li, L. Wang, and E. Sung, “AdaBoost with SVM-based component classifiers,” Eng. Appl. Artif. Intell., vol. 21, no. 5, pp. 785–795, Aug. 2008, doi: 10.1016/j.engappai.2007.07.001.
P. N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining. in Pearson International Edition. Pearson Addison Wesley, 2006. [Online]. Available: https://books.google.co.id/books?id=_XdrQgAACAAJ
K. Kumar and R. Chezian, “Support vector machine and K- nearest neighbor based analysis for the prediction of hypothyroid,” Int. J. Pharma Bio Sci., vol. 5, pp. B447–B453, Jan. 2014.
H. A. Prihanditya and A. Alamsyah, “The Implementation of Z-Score Normalization and Boosting Techniques to Increase Accuracy of C4.5 Algorithm in Diagnosing Chronic Kidney Disease,” J. Soft Comput. Explor., vol. 1, no. 1, Sep. 2020, doi: 10.52465/joscex.v1i1.8.
A. Lestari and A. Alamsyah, “Increasing Accuracy of C4.5 Algorithm Using Information Gain Ratio and Adaboost for Classification of Chronic Kidney Disease,” J. Soft Comput. Explor., vol. 1, no. 1, Sep. 2020, doi: 10.52465/joscex.v1i1.6.
N. Reska and K. Tsabita, “Comparison of KNN, naive bayes, and decision tree methods in predicting the accuracy of classification of immunotherapy dataset,” J. Student Res. Explor., vol. 1, no. 2, pp. 104–121, Jul. 2023, doi: 10.52465/josre.v1i2.170.
A. S. Levey and J. Coresh, “Chronic kidney disease,” Lancet, vol. 379, no. 9811, pp. 165–180, Jan. 2012, doi: 10.1016/S0140-6736(11)60178-5.
S. Beniwal and J. Arora, “Classification and feature selection techniques in data mining,” Int. J. Eng. Res. Technol., vol. 1, Aug. 2012.
M. A. Islam, M. Z. H. Majumder, and M. A. Hussein, “Chronic kidney disease prediction based on machine learning algorithms,” J. Pathol. Inform., vol. 14, p. 100189, 2023, doi: 10.1016/j.jpi.2023.100189.
J. Qezelbash-Chamak, S. Badamchizadeh, K. Eshghi, and Y. Asadi, “A survey of machine learning in kidney disease diagnosis,” Mach. Learn. with Appl., vol. 10, p. 100418, Dec. 2022, doi: 10.1016/j.mlwa.2022.100418.
U. I. Larasati, M. A. Muslim, R. Arifudin, and A. Alamsyah, “Improve the accuracy of support vector machine using chi square statistic and term frequency inverse document frequency on movie review sentiment analysis,” Sci. J. Informatics, vol. 6, no. 1, pp. 138–149, 2019.
R. Jensen and Q. Shen, Computational Intelligence and Feature Selection. Wiley, 2008. doi: 10.1002/9780470377888.
M. Mirqotussa’adah, M. A. Muslim, E. Sugiharti, B. Prasetiyo, and S. Alimah, “Penerapan Dizcretization dan Teknik Bagging Untuk Meningkatkan Akurasi Klasifikasi Berbasis Ensemble pada Algoritma C4.5 dalam Mendiagnosa Diabetes,” Lontar Komput. J. Ilm. Teknol. Inf., p. 135, Aug. 2017, doi: 10.24843/LKJITI.2017.v08.i02.p07.
E. Listiana and M. A. Muslim, “Penerapan Adaboost untuk Klasifikasi Support Vector Machine Guna Meningkatkan Akurasi pada Diagnosa Chronic Kidney Disease,” Seminar Nasional Teknologi dan Informatika 2017. Indonesia, 2017.
E. Prasetyo, Data mining : Mengolah data menjadi informasi menggunakan matlab. Yogyakarta, 2014.
B. Wu and R. Nevatia, “Cluster Boosted Tree Classifier for Multi-View, Multi-Pose Object Detection,” in 2007 IEEE 11th International Conference on Computer Vision, IEEE, 2007, pp. 1–8. doi: 10.1109/ICCV.2007.4409006.
A. Aziz, R. Saptono, and K. P. Suryajaya, “Implementasi Vector Space Model dalam Pembangkitan Frequently Asked Questions Otomatis dan Solusi yang Relevan untuk Keluhan Pelanggan,” Sci. J. Informatics, vol. 2, no. 2, p. 111, Feb. 2016, doi: 10.15294/sji.v2i2.5076.
F. Gorunescu, Data Mining: Concepts, models and techniques. 2011.
N. Anam and P. Hendikawati, “Aplikasi Matrix Labolatory untuk Perhitungan Sistem Antrian dengan Server Tunggal dan Majemuk,” Sci. J. Informatics, vol. 1, no. 1, pp. 65–78, May 2015, doi: 10.15294/sji.v1i1.3642.
A. Y. Nugroho, A. Suyitno, and R. Arifudin, “PERBANDINGAN ALGORITMA BRANCH AND BOUND DAN ALGORITMA GENETIKA UNTUK MENGATASI TRAVELLING SALESMAN PROBLEM (TSP) (Studi Kasus PT. JNE Semarang),” Unnes J. Math., vol. 5, no. 2, pp. 135–143, 2017, doi: https://doi.org/10.15294/ujm.v5i2.13123.
A. E. Pramadhani and T. Stiadi, “Penerapan Data mining Untuk Klasifikasi Prediksi Penyakit ISPA (Infeksi Saluran Pernafasan Akut) Dengan Algoritma Decision Tree (ID3),” J. Sarj. Tek. Inform., vol. 2, no. 1, pp. 831–839, 2014.