2026-04-17T19:10:19Zhttps://recercat.cat/oai/requestoai:recercat.cat:10230/425472025-12-19T20:30:07Zcom_2072_6col_2072_452954
RECERCAT
author
Phloyngam, Naphatthara
2019-10-29T11:01:42Z2019-10-29T11:01:42Z2019
Treball fi de màster de: Master in Intelligent Interactive SystemsTutors: Karim Lekadir, Carlos Martín IslaIdentifying the correlations between radiomics and additional medical, health and
lifestyle factors such as sex, age, BMI, etc. may help in discovering the significant
hidden patterns of data and realizing the causes of the diseases. Also, knowing the
risks in advance is a useful piece of supplementary information which may be used
in addition to medical intervention resulting in preventative measures to reduce the
level of risk or to control prescribed treatments.
In the radiomics and the clinical outcomes datasets, it is hard to identify their correlations
due to the complexity of data, computationally expensive and high number
of possible combination among the choices. Therefore, data pre-processing to keep
only the potential features and data cleaning to deal with missing or non-informative
values are mandatory steps to perform. In addition, applying the powerful Machine
Learning algorithms help to bring the results that even non-specialists in the field
could discover and understand.
This thesis facilitates the discovery of these correlations through the design and
development of an intuitive and interactive web-based tool which dynamically displays
the radiomic feature set alongside the additional medical, health and lifestyle
factors feature set based on the contents of radiomics and clinical data files. The
tool also provides a visualization of the correlation results in an easy to interpret
and meaningful way allowing for effective exploration of any correlations in addition
to cardiovascular risk score calculation.
Atribución-NoComercial-SinDerivadas 3.0 España http://creativecommons.org/licenses/by-nc-nd/3.0/es/ info:eu-repo/semantics/openAccess
Imatges tridimensionals en biologia
Sistema cardiovascular -- Malalties
Aprenentatge automàtic
Cardiovascular disease
Radiomics
Machine learning
Visualization
A computational and visualisation tool for investigating associations between cardiac
radiomics, risk factors and clinical data
info:eu-repo/semantics/masterThesis