An improved feature extraction approach based on rough sets for the medical diagnosis

This paper presents a novel approach based on Rough Sets to extract the complicated features from the medical diagnosis corpus. Some symptoms or basic features in the medical diagnosis are usually correlated. In general, the combinations of several basic symptoms may represent the disease more precision. However, the overmuch feature can reduce the generalization ability, or even many unfit features as the noise can decrease the model's performance. This paper proposes to apply the rough set theory to mine the complicated features, even from noise or inconsistent corpus. Secondly, these complex features are added into the Maximum Entropy model or Support Vector Machine etc. as a new kind of features, consequently, the feature weights can be assigned according to the performance of the whole model. The experiments in the Liver-disorders repository show that our method can improve the Maximum Entropy model by the precision 3.51%, improve the Support Vector Machine model by the precision 3.05%, improve the Naive Bayes model by the precision 3.59%, and improve the Bayes and GoodTuring model by the precision 3.59%.