Breast cancer is a prevalent type of cancer that primarily affects females. Extensive research has been conducted in the field of breast cancer, and with the advancements in technology, the early detection of this disease has become possible through the utilization of artificial intelligence or machine learning techniques. The objective of this study is to assess the accuracy of predicting the recurrence of breast cancer by employing the k-Nearest Neighbor (k-NN) algorithm. The k-NN classifier is a straightforward and versatile approach to classification, which often demonstrates comparable performance to more intricate machine-learning algorithms. The effectiveness of k-NN classifiers is closely associated with the selection of a suitable distance or similarity measure. Therefore, it is crucial to investigate the impact of employing various distance measures when analyzing biomedical data. The findings of this study indicate that the k-NN algorithm, utilizing diverse distance measurements, yields the most favorable outcomes in terms of accurately predicting the recurrence of breast cancer.

Keywords: Breast Cancer, KNN, Distance Measurements, Recurrence Prediction