On Applying Probabilistic Logic Programming to Breast Cancer Data

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Real, JC (Author) Other; Ines Dutra (Author) FCUP; Ricardo Rocha (Author) FCUP

Conference proceedings International

Title: Inductive Logic Programming - 27th International Conference, ILP 2017, Orléans, France, September 4-6, 2017, Revised Selected Papers

Pages: 31-45

27th International Conference on Inductive Logic Programming, ILP 2017

4 September 2017 through 6 September 2017

Indexing

Scopus - 2 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-00N-R72

DOI: 10.1007/978-3-319-78090-0_3

Abstract (EN): Medical data is particularly interesting as a subject for relational data mining due to the complex interactions which exist between different entities. 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 for this task, since it makes it possible to combine the relational nature of this field with the ambiguity inherent in human interpretation of medical imaging. This work presents a PILP setting for breast cancer data, where several clinical and demographic variables were collected retrospectively, and new probabilistic variables and rules reflecting domain knowledge were introduced. A PILP predictive model was built automatically from 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. © Springer International Publishing AG, part of Springer Nature 2018.

Language: English

Type (Professor's evaluation): Scientific