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首页> 外文期刊>The British Journal of Nutrition >A data mining approach to investigate food groups related to incidence of bladder cancer in the BLadder cancer Epidemiology and Nutritional Determinants International Study
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A data mining approach to investigate food groups related to incidence of bladder cancer in the BLadder cancer Epidemiology and Nutritional Determinants International Study

机译：一种探讨膀胱癌流行病学和营养决定因素国际研究中膀胱癌发生率与膀胱癌发生率相关的食物群体的数据挖掘方法

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摘要

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.
机译：目前，饮食和膀胱癌（BC）的分析主要是基于各种食物的摄入量。对食品组合的审查提供了应对饮食的复杂性和不可预测性的范围，并旨在克服营养素和食物的局限性的局限性。本文旨在展示监督数据采矿方法的可用性，以提取与BC相关的食品团体。为了导出与BC风险相关的关键食物组，我们将数据挖掘技术C5.0应用于膀胱癌流行病学和营养决定因素研究中的10倍交叉验证，包括来自十八个病例控制和一个嵌套案件的数据 - 队列研究，损害了8320年的公元前3151名参与者。膳食数据，在Eurocode 2核心分类码本的11个主要食物组上，可提供相关的非饮食数据（即性别，年龄和吸烟状态）。主要是提取五个关键食物群;按重要性，饮料（非牛奶）;谷物和谷物产品;蔬菜和蔬菜产品;脂肪，油脂及其产品;肉类和肉类产品与BC风险有关。由于这些食物团体与先前提出的BC相关膳食因素相对应，因此数据挖掘似乎是营养流行病学领域的有希望的技术，值得进一步检查。

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