SMURF: Systematic Methodology for Unveiling Relevant Factors in Retrospective Data on Chronic Disease Treatments

SMURF: Systematic Methodology for Unveiling Relevant Factors in Retrospective Data on Chronic Disease Treatments

Deciding on the continuous treatment of chronic diseases is vital in terms of economy, quality of life, and time. We present a holistic data mining approach that addresses the prediction of the therapeutic response in a panoramic and feedback way while unveiling relevant medical factors. Panoramic p...

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Bibliographic Details
Main Authors: Franklin Parrales Bravo, Alberto A. Del Barrio Garcia, Ana Beatriz Gago Veiga, Maria Mercedes Gallego De La Sacristana, Marina Ruiz Pinero, Angel Guerrero Peral, Saso Dzeroski, Jose L. Ayala
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Multi-target prediction
classification algorithms
data mining
simulated annealing
Online Access:https://ieeexplore.ieee.org/document/8756249/

Internet

https://ieeexplore.ieee.org/document/8756249/

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