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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 1997â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 â  age groupsâ and âraceâ 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.

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