Ovarian Cancer Identification using One-Pass Clustering and k-Nearest Neighbors
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.
Author: Isye Arieshanti, Yudhi
Purwananto, Handayani Tjandrasa
Journal Code: jptkomputergg130112