A Fuzzy Pattern Application in Multiple Disease Diagnosis on Large Database: Review

Basically, the Medical diagnosis process can be interpreted as a decision making process, during which the physician induce the diagnosis of a new and unknown case from an available set of clinical data and from his/her clinical experience. Data mining techniques can extract relationships and patterns holding in this wealth of data, and thus be helpful in understanding the progression of diseases and the efficacy of the associated therapies. Traditionally the enormous quantities of medical data are utilized only for clinical and short term use. Medi-Query puts to use this vast storage of information so that diagnosis based on this historical data can be made. There are systems to predict diseases of the heart, brain and lungs based on past data collected from the patients. We focus on computing the probability of occurrence of a particular ailment from the medical data by mining it using a unique algorithm which increases accuracy of such diagnosis by combining Neural Networks, Bayesian Classification and Differential Diagnosis all integrated into one single approach. The system uses a Service Oriented Architecture (SOA) wherein the system components of diagnosis, information portal and other miscellaneous services provided are coupled.