A Banerjee

F Amersi

F Bagherzadeh-Khiabani

J Dean

J Schneider

J Zhang

K Thangavel

L Polkowski

M Guller

M Porta

M Woodward

NX Vinh

SJ Mooney

T Zhai

Z Pawlak

English

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.</p

Chelly Dagdia, Zaineb

Zarges, Christine

Schannes, Benjamin

Micalef, Martin

Galiana, Lino

Rolland, Benoît

de Fresnoye, Olivier

Benchoufi, Mehdi

Aberystwyth Research Portal

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

Crossref

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

https://pure.aber.ac.uk/portal/files/28897502/ECML2018_Epidemium.pdf

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

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Aberystwyth Research Portal

Aberystwyth Research Portal

Crossref