Artif Intell Med - Impact of precision of Bayesian network parameters on accuracy of medical diagnostic systems.

Tópicos

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Resumo

JECTIVE: One of the hardest technical tasks in employing Bayesian network models in practice is obtaining their numerical parameters. In the light of this difficulty, a pressing question, one that has immediate implications on the knowledge engineering effort, is whether precision of these parameters is important. In this paper, we address experimentally the question whether medical diagnostic systems based on Bayesian networks are sensitive to precision of their parameters.METHODS AND MATERIALS: The test networks include Hepar II, a sizeable Bayesian network model for diagnosis of liver disorders and six other medical diagnostic networks constructed from medical data sets available through the Irvine Machine Learning Repository. Assuming that the original model parameters are perfectly accurate, we lower systematically their precision by rounding them to progressively courser scales and check the impact of this rounding on the models' accuracy.RESULTS: Our main result, consistent across all tested networks, is that imprecision in numerical parameters has minimal impact on the diagnostic accuracy of models, as long as we avoid zeroes among parameters.CONCLUSION: The experiments' results provide evidence that as long as we avoid zeroes among model parameters, diagnostic accuracy of Bayesian network models does not suffer from decreased precision of their parameters.

Resumo Limpo

jectiv one hardest technic task employ bayesian network model practic obtain numer paramet light difficulti press question one immedi implic knowledg engin effort whether precis paramet import paper address experiment question whether medic diagnost system base bayesian network sensit precis parametersmethod materi test network includ hepar ii sizeabl bayesian network model diagnosi liver disord six medic diagnost network construct medic data set avail irvin machin learn repositori assum origin model paramet perfect accur lower systemat precis round progress courser scale check impact round model accuracyresult main result consist across test network imprecis numer paramet minim impact diagnost accuraci model long avoid zero among parametersconclus experi result provid evid long avoid zero among model paramet diagnost accuraci bayesian network model suffer decreas precis paramet

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