An Explainable Artificial Immune System (XAIS) for Classification in Biomedical Classification Tasks

Biomedical datasets often contain noise, missing values, imbalance, and heterogeneous feature structures, making them difficult to model reliably and complicating the extraction of discriminative patterns required for effective classification. Although modern machine-learning models can achieve strong performance on such data, many of these approaches operate as opaque systems, offering little insight into how decisions are produced—an essential requirement in biomedical applications. This work introduces the Explainable Artificial Immune System (XAIS), an immune-inspired classification model that delivers prototype-based explanations derived from similarity-driven antibody responses and complemented by performance-aware indicators, providing users with direct evidential insight into each decision. XAIS was evaluated on eight publicly available biomedical datasets using stratified 5-fold cross-validation and compared against standard machine-learning classifiers. The results show that XAIS attains competitive predictive performance while offering structured, instance-level evidential explanations, underscoring its potential as a transparent and trustworthy foundation for biomedical decision-support systems. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1278Dimensions.Open Alex.