RESUMEN
Focusing on the issue that the naive Bayes model(NBM)in outpatient intelligent diagnosis,it is not effective to distinguish between different types of symptoms involved in a different range of subjects.An improved algorithm for the naive Bayes method is proposed,Introducing IDF factor,Provide different weights for different symptom types.First of all,based on authoritative medical literature,Collected and sorted the related corpus of diagnostics as a training data set,Then,based on the naive Bayes method,the priori probability and the class conditional probability are calculated,Trained the IDF factors for differ-ent symptoms,Finally,IDF factor is introduced to different combination of symptoms in classification judgment,to smoothed the different types of symptoms.In the accuracy comparison experiment of intelligent diagnosis,the recall rate of the improved algo-rithm is up about 11％,obviously higher than the naive Bayes method.