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本文引用的文献

1
A fuzzy-genetic approach to breast cancer diagnosis.一种用于乳腺癌诊断的模糊遗传方法。
Artif Intell Med. 1999 Oct;17(2):131-55. doi: 10.1016/s0933-3657(99)00019-6.

K近邻算法在乳腺癌诊断问题上的应用。

Application of K-nearest neighbors algorithm on breast cancer diagnosis problem.

作者信息

Sarkar M, Leong T Y

机构信息

Medical Computing Laboratory, Department of Computer Science, School of Computing, National University of Singapore, Lower Kent Ridge Road, Singapore: 119260.

出版信息

Proc AMIA Symp. 2000:759-63.

PMID:11079986
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2243774/
Abstract

This paper addresses the Breast Cancer diagnosis problem as a pattern classification problem. Specifically, this problem is studied using the Wisconsin-Madison Breast Cancer data set. The K-nearest neighbors algorithm is employed as the classifier. Conceptually and implementation-wise, the K-nearest neighbors algorithm is simpler than the other techniques that have been applied to this problem. In addition, the Knearest neighbors algorithm produces the overall classification result 1.17% better than the best result known for this problem.

摘要

本文将乳腺癌诊断问题视为一个模式分类问题。具体而言，使用威斯康星大学麦迪逊分校乳腺癌数据集对该问题进行研究。采用K近邻算法作为分类器。从概念和实现角度来看，K近邻算法比应用于该问题的其他技术更简单。此外，K近邻算法产生的总体分类结果比该问题已知的最佳结果高出1.17%。