Research on an Improved SVM-RF-Based Risk Assessment Algorithm for Infectious Substances at Port of Entry

The wide variety of infectious substances encountered at ports of entry, coupled with complex risk profiles and the challenges of subjective identification, make it difficult for assessors to conduct rapid, accurate, and objective evaluations, particularly given limitations in expertise and experience. To address this challenge and to develop a highly generalizable risk assessment model for infectious substances, this study draws on a five-year case database of risk incidents at Beijing Customs ports. Frequency analysis was first employed to identify key risk factors associated with infectious substances entering ports. Subsequently, risk assessment models were constructed using decision tree, random forest, and support vector machine algorithms, as well as an improved SVM-RF algorithm, followed by an analysis of feature importance. The results demonstrate that the improved SVM-RF algorithm achieved superior generalization performance, with an evaluation accuracy of 0.93. To further validate its applicability and feasibility, the improved model was applied to real cases of infectious substances intercepted at Beijing Customs ports, where its risk level classifications were consistent with expert assessments. These findings provide a valuable reference for improving the customs safety assessment system for special biological resources and for mitigating the risks posed by infectious substances at ports of entry.