In clinical medicine ,data mining deals with learning models to predict patient's health. The models can be dedicated to support clinicians in diagnostic and monitoring tasks. Data mining methods are commonly applied in clinical contexts to analyze retrospective data, thus giving healthcare professionals the opportunity to exploit enormous amounts of data routinely collected during their day-by-day activity. Nowadays, clinicians can take advantage of data mining techniques to deal with the huge amount of research results obtained by molecular medicine such as genomic signatures or genetic which may allow transition from population-based to personalized medicine.The different classification and prediction models can be devoted to support medical practitioners in diagnosis and formation of treatment plans. There is need of powerful data analysis tool to extract useful knowledge from huge amount of data available in health care field. Last few years, heart disease is the major cause of death all over the world. In heart disease diagnosis and treatment, single data mining techniques are showing satisfactory level of accuracy. Nowadays, researchers are experimenting the deployment of hybrid data mining techniques showing great level of accuracy. In this paper,single data mining techniques like Naive base,Decision tree, Association rule, Neural network and Regression are studied and compared with hybrid data mining algorithm to achieve an efficient results in heart disease diagnosis and to formulate treatment plan.

Hybrid Data Mining Genomic Retrospective Monitoring