Diabetic Prognosis through Data Mining Methods and Techniques

Data mining now-a-days plays an important role in prediction of diseases in health care industry. Data mining is the process of selecting, exploring, and modeling large amounts of data to discover unknown patterns or relationships useful to the data analyst. Medical data mining has emerged impeccable with potential for exploring hidden patterns from the data sets of medical domain. These patterns can be utilized for fast and better clinical decision making for preventive and suggestive medicine. However raw medical data are available widely distributed, heterogeneous in nature and voluminous for ordinary processing. Data mining and Statistics can collectively work better towards discovering hidden patterns and structures in data. In this paper, two major Data Mining techniques v.i.z., FP-Growth and Apriori have been used for application to diabetes dataset and association rules are being generated by both of these algorithms.