Natural Neighbor Algorithm for Breast Cancer Diagnosis

Abstract

Machine learning to diagnose breast cancer is an important, real-world medical problem. As one of the most popular machine learning algorithm, K-Nearest Neighbors (KNN) algorithm is widely used in breast cancer classification and can provide high classification accuracy and effective diagnostic capabilities. However, selection of parameter K is still an unsolved problem for K-Nearest Neighbors (KNN). To address the problem, we introduce a novel neighbor form, Natural Neighbor (NaN), which is obtained adaptively by its search algorithm. We firstly use a noise filter, which called edited natural neighbor algorithm (ENaN), to eliminate the noises and global outliers of Wisconsin’s breast cancer prognosis dataset. Then, cleaned data set and natural neighbor algorithm are used to construct a new non parameter diagnostic system. The main advantages of the proposed algorithm are that it does not need any parameters and can maintain high classification accuracy. Experiments show that the classification accuracy of proposed method is similar to the highest classification accuracy obtained by traditional K-Nearest Neighbors algorithm with different K values.