FireAntGA-ImmunoModel: An explainable bio-inspired hybrid framework for optimized immunodiagnostic classification

Key Points

• Immunodiagnostic classification is successfully optimized through bio-inspired algorithms.
• The hybrid framework employs explainable AI techniques for improved interpretability and user trust.
• Analysis demonstrates significant improvements in classification metrics over traditional methods.
• The approach highlights the necessity of explainable models in clinical decision-making processes.