A case study on the relationship between risk assessment of scientific research projects and related factors under the Naive Bayesian algorithm.

ABSTRACT: This paper delves into the nuanced dynamics influencing the outcomes of risk assessment (RA) in scientific research projects (SRPs), employing the Naive Bayes algorithm. The methodology involves the selection of diverse SRPs cases, gathering data encompassing project scale, budget investment, team experience, and other pertinent factors. The paper advances the application of the Naive Bayes algorithm by introducing enhancements, specifically integrating the Tree-augmented Naive Bayes (TANB) model. This augmentation serves to estimate risk probabilities for different research projects, shedding light on the intricate interplay and contributions of various factors to the RA process. The findings underscore the efficacy of the TANB algorithm, demonstrating commendable accuracy (average accura

SUBMITTER: Dong X 

PROVIDER: S-EPMC11002027 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature