On Applying Probabilistic Logic Programming to Breast Cancer Data
Joana Côrte-Real, Inês Dutra and Ricardo Rocha
September 2017
Abstract
Medical data is particularly interesting as a subject for relational
data mining due to the complex interactions which exist between
different en tities. Furthermore, the ambiguity of medical imaging
causes interpretation to be complex and error-prone, and thus
particularly amenable to improvement through automated decision
support. Probabilistic Inductive Logic Programming (PILP) is a
particularly well-suited tool f or this task, since it makes it
possible to combine the relational nature of this field with the
ambiguity inherent in human interpretation of me dical imaging. This
work presents a PILP setting for breast cancer data, where several
clinical and demographic variables were collected retrospe ctively,
and new probabilistic variables and rules reflecting domain knowledge
were introduced. A PILP predictive model was built automatically f rom
this data and experiments show that it can not only match the
predictions of a team of experts in the area, but also consistently
reduce the error rate of malignancy prediction, when compared to other
non-relational techniques.
Bibtex
@InProceedings{corte-real-ilp17,
author = {J. Côrte-Real and I. Dutra and R. Rocha},
title = {{On Applying Probabilistic Logic Programming to Breast Cancer Data}},
booktitle = {Proceedings of the 27th International Conference on Inductive Logic Programming (ILP 2017)},
pages = {31--45},
number = {10759},
series = {LNAI},
publisher = {Springer},
editor = {N. Lachiche and C. Vrain},
month = {September},
year = {2017},
address = {Orléans, France},
note = {Published in 2018},
}
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