A Survey on Data Mining Techniques and their Comparison Approaches for Healthcare

Data Mining (DM) has become important tool in business and related areas and its task in the healthcare field is still being explored. DM refers to mining or discovery of new information in terms of patterns or rules from vast amounts of data. To be useful, data mining must be carried out efficiently on large files and database currently, most applications of DM in healthcare can be classified into two areas: Decision Support (DS) for clinical practice, and policy development. However, it is difficult to find experimental literature in this area since a considerable amount of existing work in DM for healthcare is theoretical in nature.DM in healthcare helps in collecting heterogeneous volume of data and is stored in an organized form. The patient’s reports are maintained and analyzed in order to relate data. In view of the large amount of medical data being generated, there is growing pressure for improved methods of data analysis and knowledge discovery using appropriate data mining techniques. [1] A proper medical database created with intention mining can provide a useful resource for data mining and knowledge discovery. The terms Knowledge Discovery in Databases (KDD) and data mining are used interchangeably. KDD is the process of finding useful information, and data mining is the process for extracting knowledge (information and patterns) derived by the KDD process using algorithms .This article explores data mining applications in healthcare. In particular, it discusses data mining and its applications within healthcare in major areas such as the evaluation of treatment effectiveness, management of healthcare, Customer Relationship Management (CRM), and the detection of fraud and abuse.