Ovarian Cancer Identification using One-Pass Clustering and k-Nearest Neighbors

Isye Arieshanti, Yudhi Purwananto, Handayani Tjandrasa

Abstract


 The identification of ovarian cancer using protein expression profile (SELDI-TOF-MS) is important to assists early detection of ovarian cancer. The chance to save patient’s life is greater when ovarian cancer is detected at an early stage. However, the analysis of protein expression profile is challenging because it has very high dimensional features and noisy characteristic. In order to tackle those difficulties, a novel ovarian cancer identification model is proposed in this study. The model comprises of One-Pass Clustering and k-Nearest Neighbors Classifier.  With simple and efficient computation, the performance of the model achieves Accuracy about 97%. This result shows that the model is promising for Ovarian Cancer identification.


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References


Zhang H, Kong B, Qu X, Jia L, Deng B, Yang Q. Biomarker discovery for ovarian cancer using SELDI-TOF-MS. Gynecol Oncol. 2006; 102(1): 61-6.

Wu SP, Lin YW, Lai HC, Chu TY, Kuo YL, Liu HS. SELDI-TOF MS profiling of plasma proteins in ovarian cancer. Taiwan J Obstet Gynecol. 2006; 45(1): 26-32.

Tang KL, Li TH, Xiong WW, Chen K. Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data. BMC Bioinformatics. 2010; 27(11): 109.

Arieshanti I, Purwananto Y, Ramadhani A, Ulinnuha M, Ulinnuha N. Comparative Study of Bancruptcy Prediction Models. TELKOMNIKA Telecommunication Computing Electronics and Control. 2013; 11(3): 591-596.

Cui E. Wide Baseline Matching Using Support Vector Regression. TELKOMNIKA Telecommunication Computing Electronics and Control. 2013; 11(3)

Gordon Whiteley. Biomarker Profiling, Discovery and Identification. Center for Cancer

Research, National Cancer Institute. Available. online at:

http://home.ccr.cancer.gov/ncifdaproteomics/ppatterns.asp

S Rieber, VP Marathe. The Single Pass Clustering Method.

D Aha, D Kibler. Instance-based learning algorithms. Machine Learning. 1991; (6) 37-66.

JD Rennie, Lawrence Shih, J Teevan, DR Karger. Tackling the Poor Assumptions of Naive Bayes Text Classifiers. ICML 2003. 616-623

Andrew McCallum and Kamal Nigam. A Comparison of Event Models for Naive Bayes Text Classification. AAAI-98 Workshop on 'Learning for Text Categorization' 1998

J Platt. Fast training of support vector machines using sequential minimal optimization. in B. Schoelkopf, C. Burges and A. Smola. Kernel Methods - Support Vector Learning. MIT press 1998

M Hall, OF L Frank, G Holmes, B Pfahringer, P Reutemann, I H Witten. The WEKA data mining software: an update. ACM SIGKDD Explorations. 2009; 11(1): 10-18

R Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers. San Mateo, CA. 1993

L Breiman. Random Forests. Machine Learning. 2001; (45): 5-32




DOI: http://doi.org/10.12928/telkomnika.v11i4.1203

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