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

Baschung, Jonas; Einsele, Farshideh (2022). Finding Correlation between Chronic Diseases and Food Consumption from 30 Years of Swiss Health Data Linked with Swiss Consumption Data using FP-Growth for Association Analysis Computer Science & Information Technology, 174, pp. 101-122. AIRCC Publishing Corporation 10.5121/csit.2022.121609

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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 menuCH 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.

Item Type:	Journal Article (Original Article)
Division/Institute:	Business School > Institute for Applied Data Science & Finance Business School > Institute for Applied Data Science & Finance > Applied Data Science Business School
Name:	Baschung, Jonas and Einsele, Farshideh
Subjects:	R Medicine > RA Public aspects of medicine R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine R Medicine > RZ Other systems of medicine T Technology > T Technology (General)
ISSN:	2231 - 5403
Publisher:	AIRCC Publishing Corporation
Submitter:	Farshideh Einsele
Date Deposited:	05 Jul 2023 11:18
Last Modified:	05 Jul 2023 11:18
Publisher DOI:	10.5121/csit.2022.121609
Uncontrolled Keywords:	Data Mining, Association Analysis, Apriori Algorithm, Diet & Chronical Diseases, Health Informatics
ARBOR DOI:	10.24451/arbor.19498
URI:	https://arbor.bfh.ch/id/eprint/19498