Introduction:Data which is stored in medical databases require extensive amount of knowledge and development of specialized tools for accessing the data, data analysis, as data is increasing and it causes difficulty in extracting useful information for decision support.Data mining has been used to uncver patterns from the large amount of stored information an then used to build predictive models.Data mining techniques in medical informatics:Data mining has become a fundamental methodology for computing applications in medical informatics. Data mining aremanifested in the areas of information management in healthcare organizations, eoidemiology,patient care and monitoring systems,, large-scale iamge analysis to information extraction and automatic identification of unknown classes.To understand medical data more clearly several data mining algorithms helped to do so by distinguishing pathological data from normal data.Technique 1:Tissue microarray provides a standardized set of images, faciilitating effective automation of the evaluation of the specimen images. It is used to evaluate hormone expression for diagnosis of breast cancer.Technique 2:Face Recognition technique has been gaining prominence with proliferationof images and is used to classify the images of the esophagus into three grades of esophagitis.Technique 3:In image processing, medicine support applications frequently require the ability to identify and local sharp discontinuous in an image for feature extraction and interpretation of image content.Applications of data mining in medical informatics:It increases the efficiency and eliminate the human factor.It reduces the time and cost.Medical decision support system: uses a multi-process automation, for example- prediction models and expert systemsIt is used to extract the new knowledge or hypothesis.It helps to analyze the quality of biomedical datasets.It helps to evaluate the probable complications