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Comput Methods Programs Biomed. 2016 Oct;134:259-65. doi: 10.1016/j.cmpb.2016.07.020. Epub 2016 Jul 9.

An immune-inspired semi-supervised algorithm for breast cancer diagnosis.

Author information

1
School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, 510006, China.
2
School of Computer Science and Education, Guangzhou University, Guangzhou, 510006, China.
3
School of Computing & Information Sciences, Florida International University, Miami, FL 33199, USA.
4
Department of Computer Science, Leshan Normal Univ., Leshan 614000, China.
5
Department of Computer Science, Leshan Normal Univ., Leshan 614000, China. Electronic address: zjd2003@163.com.

Abstract

Breast cancer is the most frequently and world widely diagnosed life-threatening cancer, which is the leading cause of cancer death among women. Early accurate diagnosis can be a big plus in treating breast cancer. Researchers have approached this problem using various data mining and machine learning techniques such as support vector machine, artificial neural network, etc. The computer immunology is also an intelligent method inspired by biological immune system, which has been successfully applied in pattern recognition, combination optimization, machine learning, etc. However, most of these diagnosis methods belong to a supervised diagnosis method. It is very expensive to obtain labeled data in biology and medicine. In this paper, we seamlessly integrate the state-of-the-art research on life science with artificial intelligence, and propose a semi-supervised learning algorithm to reduce the need for labeled data. We use two well-known benchmark breast cancer datasets in our study, which are acquired from the UCI machine learning repository. Extensive experiments are conducted and evaluated on those two datasets. Our experimental results demonstrate the effectiveness and efficiency of our proposed algorithm, which proves that our algorithm is a promising automatic diagnosis method for breast cancer.

KEYWORDS:

Artificial immune; Breast cancer diagnosis; Machine learning

PMID:
27480748
DOI:
10.1016/j.cmpb.2016.07.020
[Indexed for MEDLINE]

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