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

Evan Y. W. Yu, Anke Wesselius*, Christoph Sinhart, Alicja Wolk, Mariana Carla Stern, Xuejuan Jiang, Li Tang, James Marshall, Eliane Kellen, Piet van den Brandt, Chih-Ming Lu, Hermann Pohlabeln, Gunnar Steineck, Mohamed Farouk Allam, Margaret R. Karagas, Carlo La Vecchia, Stefano Porru, Angela Carta, Klaus Golka, Kenneth C. JohnsonSimone Benhamou, Zuo-Feng Zhang, Cristina Bosetti, Jack A. Taylor, Elisabete Weiderpass, Eric J. Grant, Emily White, Jerry Polesel, Maurice P. A. Zeegers

*Corresponding author for this work

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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 groups of the Eurocode 2 Core classification codebook, and relevant non-diet data (i.e. sex, age and smoking status) were available. Primarily, five key food groups were extracted; in order of importance, beverages (non-milk); grains and grain products; vegetables and vegetable products; fats, oils and their products; meats and meat products were associated with BC risk. Since these food groups are corresponded with previously proposed BC-related dietary factors, data mining seems to be a promising technique in the field of nutritional epidemiology and deserves further examination.

Original languageEnglish
Article number0007114520001439
Pages (from-to)611-619
Number of pages9
JournalBritish Journal of Nutrition
Issue number6
Publication statusPublished - 28 Sept 2020


  • Bladder cancer
  • Data mining
  • Food groups
  • Epidemiological studies

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