An Optimized KDD Process for Collecting and Processing Ingested and Streaming Healthcare Data
Nowadays organizations are surrounded with enormous amounts of data, losing all the important information that resides in it.
Knowledge Discovery in Databases (KDD)
can aid organizations to transform this data into valuable...
by extracting complex patterns and relationships from it. To achieve that,
various KDD techniques and tools have been proposed, resulting into impressive
outcomes in various domains, especially in healthcare. Due to the huge amount of
data available within the healthcare systems, data mining is extremely important for
the healthcare sector. However, what is of major importance as well, is the way through which the
data is collected, preprocessed and integrated with each other, considering its heterogeneous and
diverse nature and format. To address all these challenges, this paper proposes a generalized KDD
approach, which in essence constitutes a supplement of all the existing approaches that study and
analyse the data mining part of the KDD process. This approach primarily concentrates on the phases
of the selection, the preprocessing, as well as the transformation of the collected healthcare data,
which are considered to be of great importance for its successful mining, analysis, and interpretation.
The prototype of the proposed approach provides an example of the developed mechanism, explaining in deep detail its phases,
verifying its possible wide applicability and adoption in various healthcare scenarios.
Argyro Mavrogiorgou, Athanasios Kiourtis, George Manias, Dimosthenis Kyriazis