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

Mewes, Ilona; Jenzer, Helena; Einsele, Farshideh

doi:10.5220/0010160200300038

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“…Additionally, we conducted a further study using a medium-sized real-world health and nutritional data from Swiss population and gained interesting rules which showed the link between nutritional habits and chronical diseases. [24] and later another study, in which we used the same national Swiss dietary survey with a five times larger dataset (collected over 25 years) from the national Swiss health survey including demographical information [25]. Based on the finding of the previous studies, where it used the pure Apriori algorithm which resulted that some critical health-related dietary features were pruned out early in course of data mining, we have applied the Weighted Association Mining Rules (WARM) analysis to the latter study.…”

Section: Introductionmentioning

confidence: 99%

Finding Correlation between Chronical Diseases and Food Consumption from 30 Years of Swiss Health Data Linked with Swiss Consumption Data using FP-Growth for Association Analysis

Baschung¹,

Einsele²

2022

Artificial Intelligence and Fuzzy Logic System

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Objective: The objective of the study was to link Swiss food consumption data with demographic data and 30 years of Swiss health data and apply data mining to discover critical food consumption patterns linked with 4 selected chronical diseases like alcohol abuse, blood pressure, cholesterol, and diabetes. Design: Food consumption databases from a Swiss national survey menu CH were gathered along with data of large surveys of demographics and health data collected over 30 years from Swiss population conducted by Swiss Federal Office of Public Health (FOPH). These databases were integrated and Frequent Pattern Growth (FP-Growth) for the association rule mining was applied to the integrated database. Results: This study applied data mining algorithm FP-Growth for association rule analysis. 36 association rules for the 4 investigated chronic diseases were found. Conclusions: FP-Growth was successfully applied to gain promising rules showing food consumption patterns lined with lifestyle diseases and people’s demographics such as gender, age group and Body Mass Index (BMI). The rules show that men over 50 years consume more alcohol than women and are more at risk of high blood pressure consequently. Cholesterol and type 2 diabetes is found frequently in people older than 50 years with an unhealthy lifestyle like no exercise, no consumption of vegetables and hot meals and eating irregularly daily. The intake of supplementary food seems not to affect these 4 investigated chronic diseases.

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Section: Introductionmentioning

confidence: 99%