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

Einsele, Farshideh; Sadeghi-Reeves, Leila; Ingold, Rolf; Jenzer, Helena (October 2015). A Study about Discovery of Critical Food Consumption Patterns Linked with Lifestyle Diseases using Data Mining Methods In: International Conference on Health Informatics. Dallas. 21.10.2015-23.10.2015. 10.5220/0005170402390245

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Keywords: Data Mining, Association Rules, Nutritional Patterns, Knowledge Interpretation, Lifestyle Diseases, Demographic, Customer Profiles, Disease Diagnosis. 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.

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Conference or Workshop Item (Paper)


School of Health Professions


Einsele, Farshideh;
Sadeghi-Reeves, Leila;
Ingold, Rolf and
Jenzer, Helena






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Date Deposited:

20 Feb 2020 08:38

Last Modified:

20 Feb 2020 08:38

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