An intelligent mining model for medical diagnosis of heart disease based on rough set data analysis

Medical databases have accumulated large quantities of information about patients and their medical conditions. The classification of a set of objects into predefined homogenous groups is a problem with major practical interest in many fields, in particular, in medical sciences. It is well established fact that right decision at right time provides an advantage in medical diagnosis. Therefore, most important challenge is to retrieve data pattern from the accumulated voluminous data and dealing with the incomplete and vague information in classification and data analysis. Thus, the ultimate goal of this work is to present an intelligent model for mining and generating classification rules for medical diagnosis of heart disease based on rough sets theory. Rough sets with Boolean reasoning discretization algorithm is introduced to discretize the data, then the rough set reduction technique is applied to find all reducts. Finally, a set of generalized rules for heart diagnosis was extracted. The proposed model shows a higher overall accuracy rates and generate more compact rules.