Clustering and Retrieval Method of Immunological Memory Cell in Clonal Selection Algorithm

TL;DR

This paper presents a computational approach inspired by the immune system's clonal selection principle, using affinity-based clustering to improve classification of medical data, achieving high accuracy.

Contribution

It introduces a novel clustering and retrieval method for immunological memory cells within a clonal selection algorithm, explicitly modeling affinity maturation.

Findings

Achieved 99.6% classification accuracy on coronary heart disease data

Demonstrated effective affinity-based clustering of immune memory cells

Validated the method's potential for medical data classification

Abstract

The clonal selection principle explains the basic features of an adaptive immune response to a antigenic stimulus. It established the idea that only those cells that recognize the antigens are selected to proliferate and differentiate. This paper explains a computational implementation of the clonal selection principle that explicitly takes into account the affinity maturation of the immune response. Antibodies generated by the clonal selection algorithm are clustered in some categories according to the affinity maturation, so that immunological memory cells which respond to the specified pathogen are created. Experimental results to classify the medical database of Coronary Heart Disease databases are reported. For the dataset, our proposed method shows the 99.6\% classification capability of training data.