ABSTRACT
The successful application of data mining in highly visible fields like e-business, marketing and retail have led to the popularity of its use in knowledge discovery in databases (KDD) in other industries and sectors. Among sectors that are just discovering data mining are the fields of medicine and public health. Applying data mining in the medical field is a very challenging process due to the idiosyncrasies of the medical profession. [1] cites several inherent conflicts between the traditional methodologies of data mining approaches and medicine. Those inherent conflicts refer to specifics in medical research. In this sense data mining in medicine differs from standard data mining practise in the following aspects:
(a) medical data mining usually starts with a pre-hypothesis and then the results are adjusted to fit the hypothesis, and
(b) whereas traditional data mining is concerned about patterns and trends in datasets, data mining in medicine is more interested in the minority that do not conform to the patterns and trends.
