Research on Application of a Naive Bayes Algorithm Based on the Rough Set Approach

A Naive Bayes algorithm based on the rough set approach is proposed in this manuscriptand successfully applied in the hospital medical caseclassification. The core of the algorithm is to filter the redundant data and parameterize the key data in the medical cases through the approaches ofattribute reduction and system attribute significance in the rough set theory, and calculate the weights in order to achieve more satisfactory accuracyof medical case classification through the Naive Bayes algorithm. The specific steps are: firstly removing the redundant phrases from the medical cases by attribute reduction of the rough set, then calculating the weight of each attribute based onsystem attribute significance, and finally constructing the Naive Bayes algorithm model based on the weighted system attributes. Experiments have shown that the above rough set approach-based Naive Bayes algorithm model is a better classification model that achieves higher classification accuracy than other Naive Bayes algorithm models.