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.
Highlights
Breast cancer is known as a fatal disease, especially for women
Since the k value for k-nearest neighbor (kNN) cannot be determined, trial and error simulations should be done to choose the optimum value for k to yield the best accuracy
The Weka simulations result that the accuracy of kNN, naïve bayes, and support vector machine (SVM) algorithm are 77.98%, 73.65%, and 71.12% respectively
Summary
Breast cancer is known as a fatal disease, especially for women. World Health Organization (WHO) reports that globally, there are about two million breast cancer new cases and causing more than 600,000 deaths in 2018 [1]. This condition urges healthcare researchers to innovate in terms of improving people’s healthcare, with the use of technology
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