The Immune System as a Model for Pattern Recognition and Classification
- Correspondence and reprints: Jerome H. Carter, MD, Director, Section of Medical Informatics, Division of General Internal Medicine, University of Alabama—Birmingham, 614 Medical Education Building, 1813 6th Avenue South, Birmingham, AL 35294. e-mail: 〈jcarter{at}gim.dom.uab.edu〉
- Received 22 February 1999
- Accepted 20 September 1999
Abstract
Objective To design a pattern recognition engine based on concepts derived from mammalian immune systems.
Design A supervised learning system (Immunos-81) was created using software abstractions of T cells, B cells, antibodies, and their interactions. Artificial T cells control the creation of B-cell populations (clones), which compete for recognition of “unknowns.” The B-cell clone with the “simple highest avidity” (SHA) or “relative highest avidity” (RHA) is considered to have successfully classified the unknown.
Measurement Two standard machine learning data sets, consisting of eight nominal and six continuous variables, were used to test the recognition capabilities of Immunos-81. The first set (Cleveland), consisting of 303 cases of patients with suspected coronary artery disease, was used to perform a ten-way cross-validation. After completing the validation runs, the Cleveland data set was used as a training set prior to presentation of the second data set, consisting of 200 unknown cases.
Results For cross-validation runs, correct recognition using SHA ranged from a high of 96 percent to a low of 63.2 percent. The average correct classification for all runs was 83.2 percent. Using the RHA metric, 11.2 percent were labeled “too close to determine” and no further attempt was made to classify them. Of the remaining cases, 85.5 percent were correctly classified. When the second data set was presented, correct classification occurred in 73.5 percent of cases when SHA was used and in 80.3 percent of cases when RHA was used.
Conclusions The immune system offers a viable paradigm for the design of pattern recognition systems. Additional research is required to fully exploit the nuances of immune computation.
