title: An immune-inspired semi-supervised algorithm for breast cancer diagnosis.
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.
authors.affiliation: School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, 510006, China.
authors.forename: Lingxi
authors.initials: L
authors.lastname: Peng
authors.affiliation: School of Computer Science and Education, Guangzhou University, Guangzhou, 510006, China.
authors.forename: Wenbin
authors.initials: W
authors.lastname: Chen
authors.affiliation: School of Computing & Information Sciences, Florida International University, Miami, FL 33199, USA.
authors.forename: Wubai
authors.initials: W
authors.lastname: Zhou
authors.affiliation: School of Computer Science and Education, Guangzhou University, Guangzhou, 510006, China.
authors.forename: Fufang
authors.initials: F
authors.lastname: Li
authors.affiliation: Department of Computer Science, Leshan Normal Univ., Leshan 614000, China.
authors.forename: Jin
authors.initials: J
authors.lastname: Yang
authors.affiliation: Department of Computer Science, Leshan Normal Univ., Leshan 614000, China. Electronic address: zjd2003@163.com.
authors.forename: Jiandong
authors.initials: J
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000276:immunology; D005260:Female; D006801:Humans
keywords: Artificial immune; Breast cancer diagnosis; Machine learning