@misc{10481/75585,
year = {2022},
month = {4},
url = {http://hdl.handle.net/10481/75585},
abstract = {In this paper we have addressed the extraction of hidden knowledge from medical records using
data mining techniques such as association rules in conjunction with fuzzy logic in a distributed
environment. A significant challenge in this domain is that although there are a lot of studies devoted
to analysing health data, very few focus on the understanding and interpretability of the data and
the hidden patterns present within the data. A major challenge in this area is that many health data
analysis studies have focussed on classification, prediction or knowledge extraction and end users find
little interpretability or understanding of the results. This is due to the use of black-box algorithms or
because the nature of the data is not represented correctly. This is why it is necessary to focus the
analysis not only on knowledge extraction but also on the transformation and processing of the data
to improve the modelling of the nature of the data. Techniques such as association rule mining and
fuzzy logic help to improve the interpretability of the data and treat it with the inherent uncertainty
of real-world data. To this end, we propose a system that automatically: a) pre-processes the database
by transforming and adapting the data for the data mining process and enriching the data to generate
more interesting patterns, b) performs the fuzzification of the medical database to represent and
analyse real-world medical data with its inherent uncertainty, c) discovers interrelations and patterns
amongst different features (diagnostic, hospital discharge, etc.), and d) visualizes the obtained results
efficiently to facilitate the analysis and improve the interpretability of the information extracted. Our
proposed system yields a significant increase in the compression and interpretability of medical data
for end-users, allowing them to analyse the data correctly and make the right decisions. We present
one practical case using two health-related datasets to demonstrate the feasibility of our proposal for
real data.},
organization = {Junta de Andalucia	P18-RT-1765},
organization = {Ministry of Universities through the EU},
publisher = {Elsevier},
keywords = {Association rules},
keywords = {Fuzzy logic},
keywords = {Data mining},
keywords = {Medical records},
title = {A fuzzy-based medical system for pattern mining in a distributed environment: Application to diagnostic and co-morbidity},
doi = {10.1016/j.asoc.2022.108870},
author = {Fernández Basso, Carlos Jesús and Gutiérrez Batista, Karel and Morcillo Jiménez, Roberto and Vila Miranda, María Amparo and Martín Bautista, María José},
}