Identifying top-k Vital Patterns from multi-class medical data

With the development of modern science, the goal of medical research is not limit to explore a type of disease but more accurate multi-subtypes of this disease. For example breast cancer can be divided into three different subtypes: BRCA1, BRCA2 and Sporadic. Previous work only focuses on distinguishing several pairs of tumors. However, the simultaneous distinguish across multiple disease types has not been well studied yet, which is important for medical researcher. In this paper, we define VP (an acronym for ¿Vital Pattern¿) and PP (an acronym for ¿Protect Pattern¿) by a statistical metric, and propose a new algorithm to make use of the property discovery VP and PP from multiple disease types. The algorithm can generate some useful rules for medical researchers. The results demonstrate the feasibility of performing the clinically useful classification from patients of multiple pneumonia types.