Rough Set Theory as a Data Mining Technique: A Case Study in Epidemiology and Cancer Incidence Prediction

• Published in: Machine Learning and Knowledge Discovery in Databases Vol. 11053; pp. 440 - 455
• Main Authors: Chelly Dagdia, Zaineb, Zarges, Christine, Schannes, Benjamin, Micalef, Martin, Galiana, Lino, Rolland, Benoît, de Fresnoye, Olivier, Benchoufi, Mehdi
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Application; Big data; Cancer incidence prediction; Epidemiology; Feature selection; Rough set theory
• ISBN: 9783030109967; 3030109968
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-10997-4_27

A big challenge in epidemiology is to perform data pre-processing, specifically feature selection, on large scale data sets with a high dimensional feature set. In this paper, this challenge is tackled by using a recently established distributed and scalable version of Rough Set Theory (RST. It considers epidemiological data that has been collected from three international institutions for the purpose of cancer incidence prediction. The concrete data set used aggregates about 5 495 risk factors (features), spanning 32 years and 38 countries. Detailed experiments demonstrate that RST is relevant to real world big data applications as it can offer insights into the selected risk factors, speed up the learning process, ensure the performance of the cancer incidence prediction model without huge information loss, and simplify the learned model for epidemiologists. Code related to this paper is available at: https://github.com/zeinebchelly/Sp-RST.