V Congreso Latinoamericano de Ingeniería Biomédica (CLAIB2011)

Symbolic machine learning and fractal techniques to characterize cardiac rejection
Veronica Oliveira Carvalho, Leandro Alves Neves, Moacir Fernandes Godoy, Roberto Douglas Moreira, Antônio Roberto Moriel, Luiz Otávio Murta Junior

##manager.scheduler.building##: Palacio de Convenciones de La Habana
##manager.scheduler.room##: Sala 12
Fecha: 2011-05-19 12:00  – 01:30
Última modificación: 2011-04-14 11:39

Resumen


This work combines symbolic machine learning and multiscale fractal techniques to generate models that characterize cellular rejection in myocardial biopsies and that can base a diagnosis support system. The models express the knowledge by the features threshold, fractal dimension, lacunarity, number of clusters, spatial percolation and percolation probability, all obtained with myocardial biopsies processing. Models were evaluated and the most significant was the one generated by the C4.5 algorithm for the features spatial percolation and number of clusters. The result is relevant and contributes to the specialized literature since it determines a standard diagnosis protocol.