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

Isye Arieshanti (Institut Teknologi Sepuluh Nopember)
Yudhi Purwananto (Institut Teknologi Sepuluh Nopember)
Handayani Tjandrasa (Institut Teknologi Sepuluh Nopember)

TELKOMNIKA (Telecommunication Computing Electronics and Control)
Vol 11, No 4: December 2013

 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.