Open AccessKnowledge discovery for pancreatic cancer using inductive logic programming
Author(s) -
Qiu Yushan,
Shimada Kazuaki,
Hiraoka Nobuyoshi,
Maeshiro Kensei,
Ching WaiKi,
AokiKinoshita Kiyoko F.,
Furuta Koh
Publication year - 2014
Publication title -
iet systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.367
H-Index - 50
eISSN - 1751-8857
pISSN - 1751-8849
DOI - 10.1049/iet-syb.2013.0044
Subject(s) - pancreatic cancer , disease , cancer , metastasis , lymph node metastasis , inductive logic programming , computer science , modalities , medicine , machine learning , artificial intelligence , oncology , social science , sociology
Pancreatic cancer is a devastating disease and predicting the status of the patients becomes an important and urgent issue. The authors explore the applicability of inductive logic programming (ILP) method in the disease and show that the accumulated clinical laboratory data can be used to predict disease characteristics, and this will contribute to the selection of therapeutic modalities of pancreatic cancer. The availability of a large amount of clinical laboratory data provides clues to aid in the knowledge discovery of diseases. In predicting the differentiation of tumour and the status of lymph node metastasis in pancreatic cancer, using the ILP model, three rules are developed that are consistent with descriptions in the literature. The rules that are identified are useful to detect the differentiation of tumour and the status of lymph node metastasis in pancreatic cancer and therefore contributed significantly to the decision of therapeutic strategies. In addition, the proposed method is compared with the other typical classification techniques and the results further confirm the superiority and merit of the proposed method.