Ovarian cancer classification using K-Nearest Neighbor and Support Vector Machine

V. V.P. Wibowo, Z. Rustam, S. Hartini, F. Maulidina, I. Wirasati, W. Sadewo

Research output: Contribution to journalConference articlepeer-review

Abstract

Ovarian cancer is one of the common malignancies in women and a known cause of death. This condition occurs when a tumor appears from the growth of abnormal cells in the ovary. It causes about 140.000 deaths out of 225.000 cases annually. Most women with ovarian cancer do not have distinctive signs and symptoms even at the late stage. Therefore, diagnosis at an early stage is necessary because it has a significant impact on the survival rate. Machine learning with various methods can be used in the medical field to classify diseases. Among the many methods, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) were used and analyzed in this study to classify ovarian cancer. The data used were from Al Islam Bandung Hospital consisting of 203 instances with 130 labeled ovarian cancer and 73 as non-ovarian. The results showed that the KNN produced higher results than SVM with 90.47% of accuracy and 94.11% of F1-score, while SVM produced accuracy and F1-score values of 90.47% and 92.30% respectively.

Original languageEnglish
Article number012007
JournalJournal of Physics: Conference Series
Volume1821
Issue number1
DOIs
Publication statusPublished - 29 Mar 2021
Event6th International Conference on Mathematics: Pure, Applied and Computation, ICOMPAC 2020 - Surabaya, Virtual, Indonesia
Duration: 24 Oct 2020 → …

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