Breast tumor malignancy modelling using evolutionary neural logic networks.
The present work proposes a computer assisted methodology for the effective modelling of the diagnostic decision for breast tumor malignancy. The suggested approach is based on innovative hybrid computational intelligence algorithms properly applied in related cytological data contained in past medical records. The experimental data used in this study were gathered in the early 1990s in the University of Wisconsin, based in post diagnostic cytological observations performed by expert medical staff. Data were properly encoded in a computer database and accordingly, various alternative modelling techniques were applied on them, in an attempt to form diagnostic models. Previous methods included standard optimisation techniques, as well as artificial intelligence approaches, in a way that a variety of related publications exists in modern literature on the subject. In this report, a hybrid computational intelligence approach is suggested, which effectively combines modern mathematical logic principles, neural computation and genetic programming in an effective manner. The approach proves promising either in terms of diagnostic accuracy and generalization capabilities, or in terms of comprehensibility and practical importance for the related medical staff.
Tsakonas
Athanasios
A
Artificial Intelligence and Information Analysis Laboratory, Department of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
Dounias
Georgios
G
Panagi
Georgia
G
Panourgias
Evangelia
E
D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D008128:Logic; D008954:Models, Biological