Ensemble Classifier untuk Klasifikasi Kanker Payudara
DOI:
https://doi.org/10.25299/itjrd.2019.vol4(1).3540Keywords:
klasifikasi, kanker payudara, ensemble classfier, ELM, SVM, kNNAbstract
Kanker payudara merupakan jenis kanker yang paling banyak diderita oleh kaum wanita di Indonesia. Penyakit tersebut dapat berakibat pada kematian jika terlambat ditangani. Oleh karena itu, deteksi dini kanker payudara merupakan langkah awal untuk menyelamatkan nyawa pasien. Pada penelitian ini telah dilakukan klasifikasi kanker payudara berdasarkan data anthopometric serta data dari hasil tes darah rutin menggunakan single classifier (ELM, SVM dan kNN) dan ensemble classifier yang menggabungkan ketiga algoritma tersebut dengan penentuan kelas majority voting. Pembagian data dilakukan dengan three way data split. Hasil eksperimen menunjukkan bahwa saat menggunakan keseluruhan fitur penggunaan ensemble classifier lebih baik daripada single classifier dalam hal akurasi maupun G-mean. Namun, saat menggunakan 4 fitur terbaik (resistin, glucose, age, dan BMI) penggunaan ensemble classifier sedikit lebih baik dalam hal G-mean. Hal ini disebabkan minimnya diversity di antara classifier sehingga saat digabungkan tidak mampu memperbaiki hasil.
Downloads
References
American Cancer Society, Cancer Facts & Figures 2019. Atlanta: American Cancer Society, 2019.
Kementerian Kesehatan, “Hari Kanker Sedunia 2019,” 2019. [Online]. Available: http://sehatnegeriku.kemkes.go.id/baca/fokus-utama/20190131/2329273/hari-kanker-sedunia-2019. [Accessed: 21-Jul-2019].
T. R. Golub et al., “Molecular Classification of Cancer : Class Discovery and Class Prediction by Gene Expression Monitoring,” vol. 286, no. October, pp. 531–537, 1999.
M. K. Kerr and G. A. Churchill, “Experimental Design for Gene Expression Microarrays,” Biostatistics, vol. 2, no. 2, pp. 183–201, 2001.
M. Patrício et al., “Using Resistin, Glucose, Age and BMI to Predict the Presence of Breast Cancer,” BMC Cancer, vol. 18(1), no. 29, pp. 1–8, 2018.
R. Zhang, G. Huang, N. Sundararajan, and P. Saratchandran, “Multicategory Classification Using an Extreme Learning Machine for Microarray Gene Expression Cancer Diagnosis,” IEEE/ACM Trans. Comput. Biol. Bioinforma., vol. 4, no. 3, pp. 485–495, 2007.
Khadijah, Rismiyati, and A. J. Mantau, “Multiclass Classification of Cancer Based on Microarray Data Using Extreme Learning Machine,” in Proceeding of The 1st International Conference on Informatics and Computational Sciences (ICICoS), 2017, pp. 159–164.
S. A. Medjahed, “Breast Cancer Diagnosis by Using k-Nearest Neighbor with Different Distances and Classification Rules,” Int. J. Comput. Appl., vol. 62, no. 1, pp. 1–5, 2013.
C. Li et al., “Using the K-Nearest Neighbor Algorithm for the Classification of Lymph Node Metastasis in Gastric Cancer,” Comput. Math. Method Med., vol. 2012, pp. 1–11, 2012.
G. Huang, Q. Zhu, and C. Siew, “Extreme Learning Machine : A New Learning Scheme of Feedforward Neural Networks,” in Proceeding of International Joint Conference on Neural Networks (IJCNN), 2004, no. July, pp. 985–990.
G.-B. Huang, H. Zhou, X. Ding, and R. Zhang, “Extreme Learning Machine for Regression and Multiclass Classification,” IEEE Trans. Syst. Man, Cybern. - Part B Cybern., vol. 42, no. 2, pp. 513–528, 2012.
J. Han and M. Kamber, Data Mining: Concepts and Techniques Second Edition. San Farnsisco: Elsevier Inc., 2006.
Z.-H. Zhou, Ensemble Methods Foundations and Algorithms, Machine Le. Boca raton: CRC Press, 2012.
M. Graña and E. Corchado, “A Survey of Multiple Classifier Systems as Hybrid Systems,” Inf. Fusion, vol. 16, pp. 3–17, 2014.
N. Joshi and S. Srivastava, “Improving Classification Accuracy Using Ensemble Learning Technique (Using Different Decision Trees),” Int. J. Comput. Sci. Mob. Comput., vol. 3, no. 5, pp. 727–732, 2014.
G. Brown, J. Wyatt, R. Harris, and X. Yao, “Diversity Creation Methods : A Survey and Categorisation,” Hournal Inf. Fusion, vol. 6, no. 1, pp. 1–28, 2005.
G. Huang, “Extreme Learning Machine - Learning Without Iterative Tuning.” Tutorial in IJCNN2012/WCCI2012, Brisbane, 2012.
I. K. Timotius and S. G. Miaou, “Arithmetic Means of Accuracies: A Classifier Performance Measurement for Imbalanced Data Set,” in International Conference on Audio, Language and Image Processing (ICALIP), 2010, pp. 1244–1251.
Downloads
Published
How to Cite
Issue
Section
License
This is an open access journal which means that all content is freely available without charge to the user or his/her institution. The copyright in the text of individual articles (including research articles, opinion articles, and abstracts) is the property of their respective authors, subject to a Creative Commons CC-BY-SA licence granted to all others. ITJRD allows the author(s) to hold the copyright without restrictions and allows the author to retain publishing rights without restrictions.
