A data mining approach to investigate food groups related to incidence of bladder cancer in the BLadder cancer Epidemiology and Nutritional Determinants International Study.

ABSTRACT: At present, analysis of diet and bladder cancer (BC) is mostly based on the intake of individual foods. The examination of food combinations provides a scope to deal with the complexity and unpredictability of the diet and aims to overcome the limitations of the study of nutrients and foods in isolation. This article aims to demonstrate the usability of supervised data mining methods to extract the food groups related to BC. In order to derive key food groups associated with BC risk, we applied the data mining technique C5.0 with 10-fold cross-validation in the BLadder cancer Epidemiology and Nutritional Determinants study, including data from eighteen case-control and one nested case-cohort study, compromising 8320 BC cases out of 31 551 participants. Dietary data, on the eleven main food

SUBMITTER: Yu EYW 

PROVIDER: S-EPMC9429981 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature