Optimizing Record Linkage Parameters with Genetic Algorithms for Health Data Integration - Pita, Pablo L.; Harron, Katie; Fernandes, Islame F. C.; Gomes, José; Rocha, Gabriel F.; Ramos, Pablo I. P.; Sena, Samila; Carreiro, Roberto; Eustáquio, Fernanda; Cardozo, George; Santos, Carlos C.; Ribeiro, Leandro Sá; et al. (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2026: Anais do XXVI Simpósio Brasileiro de Computação Aplicada à SaúdeDO - 10.5753/sbcas.2026.21436, 2026)
Record linkage is widely used to integrate administrative health databases, but its performance depends on appropriate parameterization and decision thresholds. We propose optimizing CIDACS-RL parameters using genetic algorithms to efficiently explore the parameter space. Experiments linking Brazilian live birth (SINASC) and mortality (SIM) records using a labeled dataset reduced false positives by about 90% while maintaining high recall. Precision increased from 0.70 to 0.96 and accuracy from 0.89 to 0.99, with consistent improvements across all southern Brazilian states analyzed. These results suggest that parameter optimization can improve linkage quality and the reliability of large-scale health data integration.