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I Gusti Bagus Astawa
PT Telkom Indonesia
Computer Science & IT Electrical & Electronics Engineering

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Breast cancer recurrence prediction system using k-nearest neighbor, naïve-bayes, and support vector machine algorithm I Ketut Agung Enriko; Melinda Melinda; Agnesia Candra Sulyani; I Gusti Bagus Astawa
JURNAL INFOTEL Vol 13 No 4 (2021): November 2021
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v13i4.692

Abstract

Breast cancer is a serious disease and one of the most fatal diseases in the world. Statistics show that breast cancer is the second common cancer worldwide with around two million new cases per year. Some research has been done related to breast cancer, and with the advancements of technology, breast cancer can be detected earlier by using artificial intelligence or machine learning. There are popular machine learning algorithms that can be used to predict the existence or recurrence of breast disease, for example, k-Nearest Neighbor (kNN), Naïve Bayes, and Support Vector Machine (SVM). This study aims to check the prediction of breast cancer recurrence using those three algorithms using the dataset available at the University of California, Irvine (UCI). The result shows that the kNN algorithm gives the best result in terms of accuracy to predict breast cancer recurrence.
Co-Authors Agnesia Candra Sulyani I Ketut Agung Enriko Melinda Melinda