Building an Integrated Relational Database from Swiss Nutrition National Survey and Swiss Health Datasets for Data Mining Purposes

Objective: The objective of the study was to integrate two big databases from Swiss nutrition national survey (menuCH) and Swiss health national survey 2012 for data mining purposes. Each database has a demographic base data. An integrated Swiss database is built to later discover critical food consumption patterns linked with lifestyle diseases known to be strongly tied with food consumption. Design: Swiss nutrition national survey (menuCH) with approx. 2000 respondents from two different surveys, one by Phone and the other by questionnaire along with Swiss health national survey 2012 with 21500 respondents were pre-processed, cleaned and finally integrated to a unique relational database. Results: The result of this study is an integrated relational database from the Swiss nutritional and health databases

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

Background: This article demonstrates that using data mining methods such as association analysis on an integrated Swiss database derived from a Swiss national dietary survey (menuCH) and Swiss demographical and health data is a powerful way to determine whether 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 use an integrated database of dietary and health data from a large group of Swiss population to discover critical food consumption patterns linked with lifestyle diseases known to be strongly tied with food con-sumption. Design: Food consumption databases from a Swiss national survey menuCH were gathered along with corresponding large survey of demographics and health data from Swiss population conducted by Swiss Federal Office of Public Health (FOPH). These databases were integrated and reported in a previous study as a single integrated database. A data mining method such as A-priori association analysis was applied to this integrated database. Results: 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, which is reported in this paper. As an example, the found rules of the sample show that smoking is relatively irrelevant to the high blood pressure and Diabetes, whereas consuming vegetables at regular basis reduces the risk of high Cholesterol. Conclusions: Association rule mining was successfully used to describe and predict rules linking food consumption patterns with lifestyle diseases. The gained association rules reveal that the appearance of the mutually independent nutritional characteristics in the rules are equally distributed.Furthermore, most of the sample show no chronic diseases as they smoke little and exercise regularly, which can be interpreted that sport is a strong preventive factor for chronic/lifestyle diseases. Nevertheless, a small percentage of the sample shows chronic illnesses due to unhealthy eating. Further research should consider the weighting of chronic diseases’ characteristics for them not to be pruned out early by data mining computation