title: Issues of data governance associated with data mining in medical research: experiences from an empirical study.
abstract: This chapter is a review of data mining techniques used in medical research. It will cover the existing applications of these techniques in the identification of diseases, and also present the authors' research experiences in medical disease diagnosis and analysis. A computational diagnosis approach can have a significant impact on accurate diagnosis and result in time and cost effective solutions. The chapter will begin with an overview of computational intelligence concepts, followed by details on different classification algorithms. Use of association learning, a well recognised data mining procedure, will also be discussed. Many of the datasets considered in existing medical data mining research are imbalanced, and the chapter focuses on this issue as well. Lastly, the chapter outlines the need of data governance in this research domain.
authors.affiliation: Central Queensland University, Queensland, Australia.
authors.forename: Jesmin
authors.initials: J
authors.lastname: Nahar
authors.forename: Tasadduq
authors.initials: T
authors.lastname: Imam
authors.forename: Kevin S
authors.initials: KS
authors.lastname: Tickle
authors.forename: Debora
authors.initials: D
authors.lastname: Garcia-Alonso
mesh_terms: D035843:Biomedical Research / Q000458:organization & administration*; D057225:Data Mining / Q000379:methods*; D003628:Database Management Systems / Q000458:organization & administration*; D057286:Electronic Health Records / Q000458:organization & administration*; D063025:Health Information Management / Q000458:organization & administration*; D063005:Health Information Systems / Q000458:organization & administration*; D008490:Medical Informatics / Q000458:organization & administration; D017146:Models, Organizational