Main Article Content
A vast amount of data is becoming accessible in every field due to the protocols and device availability. Thus, data mining is a comparatively imminent area of medical and healthcare research whose main goal is to acquire knowledge from large amounts of data. On the one hand, practitioners are supposed to use all of these data at the same time in their practice, Humans cannot process these large amounts of data in a short time to make diagnosis, prognoses and care schedules. Additional problems such as diverse formats of knowledge representation, semitone interoperability and patient privacy need to be addressed when applying data mining in medicine. Hence the focus of this thesis is on the data mining process and methods in medicine. From this study the methods that prevail to date in medical and health care applications are explored using data mining techniques. Moreover, the following topics are directly associated with this subject: medical data pre-processing methods, medical images processing, and multi-relational data mining. The study's key objective is to explore methodologies for applying data mining methods in medicine and healthcare, and to recognize opportunities for growing data analysis performance.