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A Study about Discovery of Critical Food Consumption Patterns Linked with Lifestyle Diseases using Data Mining Methods

机译：利用数据挖掘方法发现与生活方式疾病相关的关键食品消费模式的研究

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Background: To date, the analysis of the implications of dietary patterns on lifestyle diseases is based on data coming either from clinical studies or food surveys, both comprised of a limited number of participants. This article demonstrates that linking big data from a grocery store sales database with demographical and health data by using data mining tools such as classification and association rules is a powerful way to determine if a specific population subgroup is at particular risk for developing a lifestyle disease based on its food consumption patterns. Objective: The objective of the study was to link big data from grocery store sales with demographic and health data to discover critical food consumption patterns linked with lifestyle diseases known to be strongly tied with food consumption. Design: Food consumption databases from a publicly available grocery store database dating from 1997-1998 were gathered along with corresponding demographics and health data from the U. S. west coast, pre-processed, cleaned and finally integrated to a unique database. Results: This study applied data mining techniques such as classification and association mining analysis. Firstly, the studied population was classified according to the demographical information "age groups" and "race" and data for lifestyle diseases were correspondingly attributed. Secondly, association mining analysis was used to incorporate rules about food consumption and lifestyle diseases. A set of promising preliminary rules and their corresponding interpretation was generated and reported in the present paper. Conclusions: Association mining rules were successfully used to describe and predict rules linking food consumption patterns with lifestyle diseases. In the selected grocery store database, information about interesting aspects of the grocery store customers were found such as marital status, educational background, profession and number of children at home. An in-depth research on these attributes is needed to further expand the present demographical database. Since the search on the internet for demographical attributes back to the year of 2000 corresponding to the studied population subgroup was extremely laborious, the selected demographical attributes to prove the feasibility of the study were limited to age groups and race.
机译：背景：迄今为止，饮食模式对生活方式疾病的影响的分析是基于来自临床研究或食物调查的数据，两者都包含有限数量的参与者。本文演示了通过使用分类和关联规则等数据挖掘工具将来自杂货店销售数据库的大数据与分类和关联规则等数据采矿工具相连是一种强大的方式来确定特定人口亚组特别是发展生活方式疾病的特殊风险在其食物消费模式。目的：该研究的目的是将来自杂货店销售的大数据与人口统计和健康数据联系起来发现与已知的生活方式疾病相关的关键食品消费模式与食品消费强烈捆绑。设计：来自1997-1998的公开的杂货店数据库的食品消费数据库与来自美国西海岸的相应人口统计和健康数据一起聚集在一起，预处理，清洁，最后集成到唯一的数据库。结果：本研究应用了数据挖掘技术，如分类和关联采矿分析。首先，研究人口根据人口统计信息“年龄群”和“种族”以及生活方式疾病的数据相应地归功于。其次，使用协会采矿分析纳入食品消费和生活方式疾病的规则。在本文中产生并报告了一系列有前途的初步规则及其相应的解释。结论：协会矿业规则已成功地描述和预测将食物消费模式与生活疾病联系起来的规则。在所选的杂货店数据库中，发现了有关杂货店客户的有趣方面的信息，例如婚姻状况，教育背景，家庭儿童数量。需要对这些属性进行深入的研究，以进一步扩展当前人口统计数据库。由于互联网上的人口统计属性的搜索返回到2000年对应于学习的人口亚组的年度极为费力，因此所选人口统计属性证明该研究的可行性仅限于年龄组和种族。

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