Authors:
Marco Spruit
1
;
Thomas Dedding
1
and
Daniel Vijlbrief
2
Affiliations:
1
Department of Information and Computing Sciences, Utrecht University, The Netherlands
;
2
Department of Neonatology, Wilhelmina Children’s Hospital, University Medical Center Utrecht, The Netherlands
Keyword(s):
Applied Data Science, Meta-algorithmic Modelling, Knowledge Discovery, Domain Expertise, Healthcare, Data Analytics, CRISP-DM.
Abstract:
Knowledge Discovery (KD) and Data Mining are two well-known and still growing fields that, with the advancements of data collection and storage technologies, emerged and expanded with great strength by the many possibilities and benefits that exploring and analyzing data can bring. However, it is a task that requires great domain expertise to really achieve its full potential. Furthermore, it is an activity which is done mainly by data experts who know little about specific domains, like the healthcare sector, for example. Thus, in this research, we propose means for allowing domain experts from the medical domain (e.g. doctors and nurses) to also be actively part of the Knowledge Discovery process, focusing in the Data Preparation phase, and use the specific domain knowledge that they have in order to start unveiling useful information from the data. Hence, a guideline based on the CRISP-DM framework, in the format of methods fragments is proposed to guide these professionals throug
h the KD process.
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