An immune-inspired semi-supervised algorithm for breast cancer diagnosis. Issue 134 (October 2016)

• Record Type: Journal Article
• Authors: Peng, Lingxi; Chen, Wenbin; Zhou, Wubai; Li, Fufang; Yang, Jin; Zhang, Jiandong
• Is Part Of: Computer methods and programs in biomedicine. Issue 134(2016)
• Page Start: 259
• Page End: 265
• Publication Date: 2016-10
• Subjects: Breast cancer diagnosis -- Artificial immune -- Machine learning; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.07.020
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We investigate an immune-inspired semi-supervised learning algorithm to reduce the need for labeled data. Experimental results prove that our algorithm is a promising automatic diagnosis method for breast cancer. The proposed algorithm has clonal selection, non-linear, and such excellent immune characteristics. 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.