Health informatics is a multidisciplinary field that uses health information technology (HIT) to improve health care via any combination of higher quality, higher efficiency, and new opportunities. The availability of public health care information gives the opportunity for researchers to access and analyze the data. Data mining has been applied in health informatics in many area. Both environmental and genetic factors have roles in the development of some diseases. Complex diseases, such as Crohn’s disease or Type II diabetes, are caused by a combination of environmental factors and mutations in multiple genes. Patients who have been diagnosed with such diseases cannot easily be treated. However, many diseases can be avoided if people at high risk change their living style. How to identify their susceptibility to diseases before symptoms are found and help them make informed decisions about their health becomes an important topic in health informatics.  The susceptibility to complex diseases can be predicted through the analysis of the genetic data. With the development of DNA microarray technique, it is possible to access the human genetic information related to specific diseases. This paper uses a combinatorial method to analyze the genetic case-control data for Crohn’s disease. A distance based cluster method has been applied to publicly available genotype data on Crohn’s disease for epidemiological study and achieved a high accurate result.