Open AccessNatural Neighbor Algorithm for Breast Cancer Diagnosis
Author(s) -
Shuxiang Li,
Lijun Yang,
Peiyu Chen,
Cheng Yang
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1395/1/012003
Subject(s) - k nearest neighbors algorithm , computer science , algorithm , artificial intelligence , outlier , nearest neighbor chain algorithm , breast cancer , statistical classification , noise (video) , pattern recognition (psychology) , machine learning , data mining , cancer , cluster analysis , medicine , canopy clustering algorithm , correlation clustering , image (mathematics)
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.