Journal of Medical Systems

, Volume 35, Issue 3, pp 283–289 | Cite as

An Application of Artificial Immune Recognition System for Prediction of Diabetes Following Gestational Diabetes

  • Hung-Chun Lin
  • Chao-Ton Su
  • Pa-Chun Wang
Original Paper


Diabetes mellitus (DM) is a disease prevalent in population and is not easily perceived in its initial stage but may sway a patient very seriously in later stage. In accordance with the estimation of World Health Organization (WHO), there will be 370 million diabetics which are 5.4% of the global people in 2030, so it becomes more and more important to predict whether a pregnant woman has or is likely to acquire diabetes. This study is conducted with the use of the machine learning—Artificial Immune Recognition System (AIRS)—to assist doctors in predicting pregnant women who have premonition of type 2 diabetes. AIRS is proposed by Andrew Watkins in 2001 and it makes use of the metaphor of the vertebrate immune system to recognize antigens, select clone, and memorize cells. Additionally, AIRS includes a mechanism, limited resource, to restrain the number of memory cells from increasing uncontrollably. It has also showed positive results on problems in which it was applied. The objective of this study is to investigate the feasibility in using AIRS to predict gestational diabetes mellitus (GDM) subsequent DM. The dataset of diabetes has imbalanced data, but the overall classification recall could still reach 62.8%, which is better than the traditional method, logistic regression, and the technique which is thought as one of the powerful classification approaches, support vector machines (SVM).


Artificial Immune Recognition System (AIRS) Type 2 diabetes Vertebrate immune system Imbalanced data 



This work was supported in part by the National Science Council, Taiwan, under grant NSC-98-2221-E-007-071-MY3.


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of Industrial Engineering and Engineering ManagementNational Tsing Hua UniversityHsinchuTaiwan
  2. 2.Department of Otolaryngology, Cathay General HospitalFu Jen Catholic University School of MedicineTaipeiTaiwan
  3. 3.HsinchuRepublic of China

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