A Study about Discovery of Critical Food Consumption Patterns

Linked with Lifestyle Diseases for Swiss Population using

Data Mining Methods

Ilona Rahel Mewes

, Helena Jenzer

and Farshideh Einsele

Section of Business Information, Bern University of Applied Sciences, Switzerland

Hospital of Psychiatry, University of Zurich, Switzerland

Keywords: Data Mining, Association Analysis, Diet & Chronical Diseases, Health Informatics.

Abstract: 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 consump-

tion 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 chronical diseases as they smoke little and exercise regularly, which

can be interpreted that sport is a strong preventive factor for chronical/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 compu-

tation.

1 INTRODUCTION

Lifestyle diseases are diseases that increase in fre-

quency as countries become more industrialized and

people get more aged. Lifestyle diseases include obe-

sity, hypertension (blood pressure), heart disease,

type 2 diabetes, cancer, mental disorders and many

others. They differ from the infectious diseases origi-

nated from malnutrition, also called communicable

diseases (CD) due to their contagious, dispersive na-

ture. Lifestyle diseases are therefore among the so-

called NC (non-communicable diseases) diseases.

According to World Health Organization (WHO), the

growing epidemic of chronic diseases afflicting both

developed and developing countries are related to di-

etary and lifestyle changes (WHO, 2003).

Several researches studied the relationship be-

tween nutritional habits and lifestyle diseases aka

chronic diseases. A. Fardet and Y. Boirie have aggre-

gated 304 pooled/meta-analyses and systematic re-

views in order to obtain a qualitative overview of the

associations between 17 food and beverage groups

and the risk of diet-related chronic disease. The re-

view of these authors confirmed that plant food

groups were more protective than animal food groups

against diet-related chronic diseases. Their results

show that overweight, obesity, type 2 diabetes, cancer

and cardiovascular diseases accounted for 289 of the

pooled/meta-analyses and systematic reviews (Fardet

Mewes, I., Jenzer, H. and Einsele, F.

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

DOI: 10.5220/0010160200300038

In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 5: HEALTHINF, pages 30-38

ISBN: 978-989-758-490-9

and Boirie, 2014). Further, S. Fardet et al. conducted

additional pooled analyses and meta-analyses of co-

hort studies and randomized controlled trials that

linked fruit consumption with the risk of chronic dis-

ease and metabolic deregulation. Their results show

that the degree of processing influences the health ef-

fects of fruit-based products. Fresh and dried fruits

appeared to have a neutral or protective effect on

health, 100% fruit juices had intermediary effects,

and high consumption of canned fruit and sweetened

fruit juice was positively associated with the risk of

all-cause mortality and type 2 diabetes, respectively

(Fardet , 2019). S. Schneider and al. conducted a mini

Nutritional Assessment as a promising score for eval-

uating malnutrition in the elderly, since nutrition in-

tervention shortens the length of stay by diminishing

the rate of complication and to identify malnourished

patients and those who are at nutritional risk in order

to treat and prevent malnutrition by chronic diseases

by elderly (Schneider and Hebuterne ,2000).

Machine Learning and Data Mining methodolo-

gies for chronic diseases prediction and prevention in

relationship with nutritional habits have been ex-

plored by different researchers Internationally. S. Lee

et al conducted a study using stepwise logistic regres-

sion (SLR) analysis, decision tree, random forest, and

support vector machine as an alternative and comple-

ment to the traditional statistical approaches to iden-

tify the factors that affect the health-related quality of

life (HRQoL) of the elderly with chronic diseases and

to subsequently develop from such factors a predic-

tion model (Lee, 2014)]. D. Qudsi and al. report in

(Qudsi and Kartiwi, 2017) from a study that aims to

identify the potential benefits that data mining can

bring to the health sector, using Indonesian Health In-

surance company data as case study. Decision tree as

a classification data mining method, was used to gen-

erate the prediction model by visualizing the tree to

perform predictive analysis of chronic diseases. Z.

Lei et al report in (Lei, 2018) of studying the relation-

ship between nutritional ingredients and diseases

such as diabetes, hypertension and heart disease by

using data mining methods. They have identified the

first two or three nutritional ingredients in food that

can benefit the rehabilitation of those diseases. R.

McCabe et al. report in (McCabe, 2008) of creating a

simulation test environment using characteristic mod-

els of physician decision strategies and simulated

populations of patients with type 2 diabetes, they state

of employing a specific data mining technology that

predicts encounter-specific errors of omission in rep-

resentative databases of simulated physician-patient

encounters, and test the predictive technology in an

administrative database of real physician-patient en-

counter data. D.W. Haslam and W.P. James report in

(Haslam, 2005) of an investigation in a population -

based sample of 1140 children performed in order to

derive dietary patterns related to children's obesity

status. Their findings reveal that Rules derived

through a data mining approach revealed the detri-

mental influence of the increased consumption of

fried food, delicatessen meat, sweets, junk food and

soft drinks. K. Lange et al. state in (Lange, 2016) that

Big data studies may ultimately lead to personalized

genotype-based nutrition which could permit the pre-

vention of diet-related diseases and improve medical

therapy. A. Hearty and M. Gibney evaluate the usa-

bility of supervised data mining methods as ANNs

and decision trees to predict an aspect of dietary qual-

ity an aspect of dietary quality based on dietary intake

with a food-based coding system and a novel meal-

based coding system (Hearty, 2008). A. von Reusten

et al. used data from 23 531 participants of the EPIC-

Potsdam study to analyze the associations between 45

single food groups and risk of major chronic diseases,

namely, cardiovascular diseases (CVD), type 2 diabe-

tes and cancer using multivariable-adjusted Cox re-

gression. Their results show that higher intakes of

low-fat dairy, butter, red meat and sauce were associ-

ated with higher risks of chronic diseases (von Ru-

esten, 2013). E. Yu et al. demonstrate in (Yu, 2020)

the usability of supervised data mining methods to ex-

tract the food groups related to bladder cancer. Their

results show that beverages (non-milk); grains and

grain products; vegetables and vegetable products;

fats, oils and their products; meats and meat products

were associated with bladder cancer risk.

To gain understanding about the impact of using

data mining techniques for the analysis of lifestyle

diseases that can be influenced by nutrition, we con-

ducted a preliminary study on this matter (Einsele,

2015). In this preliminary previous study, we used a

big database gained from a grocery store chain over a

certain period along with associated health data of the

same region. Association rule mining was success-

fully used to describe and predict rules linking food

consumption patterns with lifestyle diseases. In the

current study, however, we use two real world big

databases, one from a national Swiss dietary survey

and the other from the national Swiss health survey

including demographical information and use a simi-

lar data mining approach as described in (Einsele,

2015) to gain promising association rules that show

the link between Swiss nutritional habits and chroni-

cal diseases.

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

Methods

2 DATABASE SELECTION

The data comes from the national surveys menuCH

and the health survey that was carried out in Switzer-

land.

The national food survey menuCH (BLV, Federal

Office for Food Safety and Veterinary 2020) was car-

ried out for the first time from January 2014 to Feb-

ruary 2015. Over 2000 people living in Switzerland

were asked about their eating habits and food con-

sumption. The data resulting from the survey is the

first representative, national nutritional survey data

available in Switzerland from BLV.

The second database results from the Swiss health

survey. This survey is being carried out by the federal

government every five years since 1992. In this work

the health data from 2012 is used. The data contains

data sets from over 21,000 interviewed people. This

data has already been pre-cleaned, attributes have

been partially selected from the database and the data

has been already transformed as reported in (Mewes

and Einsele, 2020).

3 DATA PREPARATION FOR

DATA MINING PROCESS

Preparatory steps had to be carried out for the data

mining processes. The Swiss data were cleaned in ad-

vance and a selection of the important attributes (table

columns) of the health and nutritional databases was

made.

In a first data selection, the attributes of the health

and nutritional data relevant to the question were se-

lected. A further reduction of the data was necessary

because the selected attributes were still too extensive

in their characteristics and the characteristics were in

a structure that did not make sense for a first data min-

ing attempt.

Our multidisciplinary team consisted of a special-

ist in health and nutrition, that enabled us to appropri-

ately assess, select and summarize the characteristics

of the attributes into categories. The aim of the further

categorization was to create several 4-8 occurrences

for each category. This was followed by the transfor-

mation of the data according to the corresponding cat-

egorization and the creation of a new relational, inte-

grated database (see Fig. 1).

4 CATEGORIZATION OF SWISS

HEALTH DATA

Categories were created on blood pressure, Choles-

terol, diabetes and alcohol consumption. Blood pres-

sure was reduced into 6 categories. The Cholesterol

data was reduced to 4 categories. The diabetes data

was reduced to 4 categories and finally alcohol con-

sumption data was reduced to 4 categories. As an ex-

ample, the alcohol consumption data was reduced as

follows:

 Daily alcohol consumption up to 18 grams,

 Daily alcohol consumption > 18-23 grams,

 Daily alcohol consumption> 23-28 grams,

 Daily alcohol consumption> 28 grams.

5 CREATION OF INTEGRATED,

RELATIONAL DATABASE

After defining the categories for each chronical dis-

ease and menuCH attributes, the data was trans-

formed according to the corresponding categorization

to an integrated, relational database. Five common

demographical attributes available in both databases

were used, such as gender, age group, household,

marital status and language to link the two databases

into an integrated relational database. Fig. 1 shows

the resulted new integrated database.

6 ASSOCIATION ANALYSIS

WITH THE A-PRIORI

ALGORITHM

The basis for the implementation of the A-priori al-

gorithm is the data with discrete sizes, which were put

in Excel tables. Each row entry in the table is a trans-

action. Several items were summarized per transac-

tion. The sum of all transactions was the population.

The aim of the association analysis is to find rules of

the form "if feature A occurs, then feature B occurs

with the probability of the confidence level" (A-> B).

The calculation parameters support, confidence and

lift were used to evaluate the rules. The algorithm

continues until no item set fulfils the minimum sup-

port (Agrawal and Srikant, 1994). Item sets for rule

HEALTHINF 2021 - 14th International Conference on Health Informatics

Figure 1: The integrated Swiss Nutrition-Health Database.

formation were selected from theses 9 items. The A-

Priori algorithm was then run a second time without

the healthy chronic disease values. The item sets with

the highest support and confidence value were se-

lected for rule formation. In this study A-Priori algo-

rithm was applied to find rules for a set of 9 items as

follows: 8 items from menuCH database and 1 item

was the categorized chronical diseases from Swiss

health database as described previously (see sec. cat-

egorization of health and menuCH data)

6.1 Exemplary Presentation of the Data

Mining Process for a Chronic

Disease: Blood Pressure

6.1.1 Step 1

In the first iteration of the a priori algorithm, the sup-

ports of all 1-Itemsets were calculated.

Table 1: Blood Pressure, 1-Itemset.

6.1.2 Step 2

In the second iteration, all 2-item combinations were

formed with the item blood pressure and the support

was calculated for all 2-item combinations. In total,

in the second iteration there were 8 times 2-item sets

for which the support was calculated. Here, a mini-

mum support of 0.01 was specified. All 2 items of the

item set blood pressure and movement with a mini-

mum support 0.01 were taken into the next iteration

(8 item sets).

Table 2: Blood Pressure, 2-Itemset with minsup-Line.

6.1.3 Step 3

The calculation of all 2-Itemsets for the further itera-

tions would have been too extensive for the scope of

this work. The item food supplements has been added

to the 2-Itemset blood pressure and exercise.

For the 3-Itemset, the supports for all different

item sets were calculated All item sets with a support

above 0.01 were included in the fourth iteration (12

item sets).

Blutdruck Transaktionen Support

nichtmedizinischbeurteiltnormal 226920 0.5641

medizinischbeurteiltnormal 114914 0.2857

nichtmedizinischbeurteiltzutief 40130 0.0998

medizinischbeurteiltzuhoch 15955 0.0397

medizinischbeurteiltzutief 2524 0.0063

nichtmedizinischbeurteiltzuhoch 1844 0.0046

Blutdruck Bewegung Transaktionen Support

nichtmedizinischbeurteiltnormal Regelmässi

206885 0.4498

medizinischbeurteiltnormal Regelmässi

105754 0.2299

nichtmedizinischbeurteiltzutief Regelmässi

37281 0.0811

medizinischbeurteiltzuhoch Regelmässi

14523 0.0316

medizinischbeurteiltzutief Regelmässi

2334 0.0051

nichtmedizinischbeurteiltnormal Unregelmäs

24155 0.0525

medizinischbeurteiltnormal Unregelmäs

10759 0.0234

nichtmedizinischbeurteiltzutief Unregelmäs

3869 0.0084

medizinischbeurteiltzuhoch Unregelmäs

1673 0.0036

medizinischbeurteiltzutief Unregelmäs

218 0.0005

nichtmedizinischbeurteiltnormal Selten–nie

(

3335 0.0073

medizinischbeurteiltnormal Selten–nie

(

2130 0.0046

nichtmedizinischbeurteiltzutief Selten–nie

(

333 0.0007

medizinischbeurteiltzuhoch Selten–nie

(

259 0.0006

medizinischbeurteiltzutief Selten–nie

(

48 0.0001

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

Methods

Table 3: Blood Pressure, 3-Itemset with minsup Line.

6.1.4 Further Steps

The item salt was added to the 3-Itemsets blood pres-

sure, exercise and food supplements. Support was cal-

culated for all item combinations of the 4-Itemsets. In

this iteration, all item sets with a minimum support of

0.005 were taken into the next iteration.

In the next iteration, the item smoking was added.

In the iteration with the 5-Itemsets, the minimum sup-

port of 0.005 was used again.

The drinks item was added to the 5-Itemsets. All

6-Itemsets with a support above 0.005 were included

in the 7th iteration.

The item warm meals was added to the 6-Itemsets.

The main meals item has been added to the 7-Itemsets

with a minimum support of 0.0025.

No minimum support was specified for the 8-

Itemsets because a total of 60 times 8-Itemsets still

had support above 0. The item vegetables has been

added to the 8-Itemsets. The support, the confidence

value and the lift were calculated for the 9-Itemsets.

6.1.5 Final Step: Building Association Rules

Only item sets with a blood pressure value “medically

assessed” were used. From these item sets, the item

sets with the highest support and confidence value

were selected in order to form rules (see Fig. 5).

The a- priori algorithm was carried out a second

time without the healthy blood pressure values. The

minimum support was set in this implementation in

all iterations at 0.00025. After calculating the support

and confidence and lift values for the 9-Itemsets. Item

sets with the highest support and confidence value

were selected for rule formation. Hence 6 rules re-

sulted.

7 RESULTS OF ASSOCIATION

MINING USING A-PRIORI

ALGORITHM

After completion of the algorithm rules were found

that show the relationship between nutrition and

chronic diseases. We report in the following gained

rules for blood pressure, Cholesterol and Diabetes.

7.1 Blood Pressure

Rule 1: 0.52% of people in the sample have a medi-

cally assessed normal blood pressure and have the

following characteristics: They do not take any die-

tary supplements. They smoked earlier; they eat

warm meals irregularly (4-7 times a week).

Rule 2: 0.16% of the people in the sample have a

medically assessed normal blood pressure and have

the following characteristics: They do not take any di-

etary supplements. They have never smoked; they

consume hot meals regularly.

Rule 3: 0.12% of the people in the sample have a

medically assessed normal blood pressure and have

the following characteristics: Dietary supplements,

they have never smoked, they consume hot meals reg-

ularly. 4% of the sample has a medically judged high

blood pressure.

Rule 4: 0.06% of the people in the sample have a

medically assessed high blood pressure and have the

following characteristics: They do not take any die-

tary supplements. They have never smoked; they con-

sume hot meals regularly.

Rule 5: 0.06% of the people in the sample have a

medically assessed high blood pressure and have the

following characteristics: They do not take any die-

tary supplements. They used to smoke; they consume

warm meals irregularly.

Rule 6: 0.05% of the people in the sample have a

medically assessed high blood pressure and have the

following characteristics: They are taking food sup-

plements. They have never smoked; they consume

hot meals regularly.

Table 4: Blood Pressure, 9-Itemset.

Blutdruck Beweg

Nahrungsergänzungs

Transaktionen Support

nichtmedizinis

Regel

NimmtkeineNahrungs 110005 0.2734

nichtmedizinis

Regel

NimmtNahrungs ergän

90692 0.2254

medizinischbe

Regel

NimmtkeineNahrungs 55890 0.1389

medizinischbe

Regel

NimmtNahrungs ergän

46556 0.1157

nichtmedizinis

Regel

NimmtNahrungs ergän

18145 0.0451

nichtmedizinis

Regel

NimmtkeineNahrungs 17958 0.0446

nichtmedizinis

Unrege NimmtkeineNahrungs 14385 0.0358

nichtmedizinis

Unrege NimmtNahrungsergän

9124 0.0227

medizinischbe

Regel

NimmtkeineNahrungs 7765 0.0193

medizinischbe

Regel

NimmtNahrungs ergän

6324 0.0157

medizinischbe

Unrege NimmtkeineNahrungs 6048 0.0150

medizinischbe

Unrege NimmtNahrungsergän

4390 0.0109

nichtmedizinis

Unrege NimmtkeineNahrungs 1935 0.0048

nichtmedizinis

Unrege NimmtNahrungsergän

1780 0.0044

medizinischbe

Regel

NimmtkeineNahrungs 1239 0.0031

medizinischbe

Regel

NimmtNahrungs ergän

1026 0.0026

Blutdruck Bewegung Nahrungsergänzungsmittel Salz Rauchen Getränke FrequenzwarmeMahlzeiten Hauptmahlzeiten Gemüse Transaktion

Support Konfiden

Lift

medizinisc Regelmässi

NimmtkeineNahrungsergän

Salzmi

Früher Wasser,Kaf

warmeMahlzeitunregelmässigFSregel./MEregel.

Gemüse

2047.00 0.0052 0.0178 1.26

medizinisc Regelmässi

NimmtkeineNahrungsergän

Salzmi

Nie Wasser,Kaf

warmeMahlzeitregelmässig(8

‐

FSregel./MEregel.

Gemüse

1945.00 0.0050 0.0169 1.29

medizinisc Regelmässi

NimmtNahrungsergänzungs

Salzmi

Nie Wasser,Kaf

warmeMahlzeitregelmässig(8

‐

FSregel./MEregel.

Gemüse

1422.00 0.0036 0.0124 0.95

HEALTHINF 2021 - 14th International Conference on Health Informatics

7.2 Cholesterol

Rule 1: 1.4% of the people in the sample have a med-

ically assessed normal cholesterol value and have the

following characteristics: They do not take any food

supplements. They smoked earlier; they eat warm

meals irregularly (4-7 times a week). They process

vegetables regularly (more than twice a week).

Rule 2: 1.3% of the sample have a medically assessed

normal cholesterol value and have the following char-

acteristics: They do not take any dietary supplements.

They have never smoked; they consume hot meals

regularly (4-7 times a week). They process vegetables

regularly (more than twice a week).

Rule 3: 0.4% of the people in the sample have a med-

ically assessed normal cholesterol value and have the

following characteristics: They do not take any food

supplements. They used to smoke, they rarely or

rarely consume hot meals. They never or rarely pro-

cess vegetables.

Rule 4: 0.1% of the people in the sample have a med-

ically assessed normal cholesterol value and have the

following characteristics: They do not take any food

supplements. They used to smoke, they rarely or rarely

consume hot meals. They process vegetables regularly.

Rule 5: 0.07% of the people in the sample have a

medically assessed high cholesterol value and have

the following characteristics: They do not take any

food supplements. They used to smoke; they consume

warm meals irregularly. Process vegetables regularly.

Rule 6: 0.05% of the people in the sample have a

medically judged high cholesterol value and have the

following characteristics: They do not take any food

supplements. They have never smoked; they consume

hot meals regularly. Vegetables process regularly.

Rule 7: 0.04% of the people in the sample have a

medically judged high cholesterol value and have the

following characteristics: They take dietary supple-

ments. They have never smoked; they consume hot

meals regularly. Process vegetables regularly.

7.3 Diabetes

Rule 1: 0.017% of people in the sample have a med-

ical diagnosis of diabetes and have the following

characteristics: They do not take any dietary supple-

ments. They have never smoked; they consume hot

meals regularly.

Rule 2: 0.015% of the people in the sample have a

medical diagnosis of diabetes and have the following

characteristics: They do not take any dietary supple-

ments. They used to smoke; they consume warm

meals irregularly.

Rule 3: 0.4% of the people in the sample have a med-

ical diagnosis of diabetes and have the following

characteristics: They are taking food supplements.

They have never smoked; they consume hot meals reg-

ularly.

8 KNOWLEDGE

INTERPRETATION

8.1 Blood Pressure

Hypertension is a disease of the organ axis of the heart

- vessels - kidneys or lungs. The heart no longer de-

livers enough cardiac output, the vessels have lost

their elasticity and the kidneys or lungs are inade-

quate, which creates a counterpressure and, in the

case of the kidney, the pressing pressure is insuffi-

cient for the excretion of metabolic end products. As

with a powerless electric motor, which is also sup-

posed to supply a blocked lawn sprinkler with water,

but is overheated and destroyed by resistance, the

heart works against resistance. It tries to generate

more strength with volume increases, the heart wall

becomes thicker and thicker until the strength is no

longer enough, and the disease is decompensated. The

system becomes insufficient. There is a high risk of

stroke (brain or coronary arteries). In the study, hy-

pertension and normal pressure were associated with

characteristics (dietary supplements, smoking, num-

ber of hot meals).

Rule 1: Energy production is sufficient to maintain

metabolic performance, i.e. Vitamins and trace ele-

ments and the oxygen supply are sufficient. Normo-

tonic do not need food supplements. Your cardiovas-

cular performance is sufficient, even if you smoke

earlier. Nicotine has not yet noticeably damaged the

lungs, or the lung tissue is regenerated. The food in-

take in this group is enough for maintaining health.

Cholesterol and fats are obviously not absorbed ex-

cessively, so that vascular damage and obviously obe-

sity are avoided.

Rule 2: The same situation as in rule 1 as normotics.

The lung tissue is even healthier in this group.

Rule 3: The same situation as in Rules 1 and 2,

whereby the dietary supplement intake is not known.

This supply is guaranteed by a balanced diet.

Rule 4: Hypertensive patients with an impairment of

the functional axis cardiovascular kidney. Probably

older people with this profile. Here, food supplements

could improve energy production (ATP). The lungs

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

Methods

are probably intact, so the kidneys have tended to be-

come insufficient, but the obesity should be reduced.

Rule 5: Hypertensive patients with an impairment of

the cardiovascular-kidney functional axis. Probably

older people with this profile. Here, food supplements

could improve energy production (ATP). The lung

function could be impaired by previous smoking, so

the pulmonary circulation could also be at high pres-

sure. Obesity should be reduced.

Rule 6: Hypertensive patients with an impairment of

the cardiovascular-kidney functional axis. Probably

older people with this profile. Here, food supplements

could improve energy production (ATP). The lung

function is probably intact (no pulmonary high pres-

sure). Obesity should be reduced.

8.2 Diabetes

There is an acquired partial loss of function of the in-

sulin-producing cells in the pancreas. Here it is possi-

ble to stimulate insulin secretion by oral means. These

then increase the glucose uptake in muscle cells and

liver cells and thus the energy production. Today

there is a new generation of antidiabetic drugs against

type 2 diabetes. They no longer influence the cells,

but act like incretin, a hormone that already plays a

role in the absorption of food from the intestine into

the circulation. The whole insulin cascade is then trig-

gered in a finely dosed manner, which is much gentler

for the remaining function of the pancreas than with

the old oral antidiabetic agents. Since glucose is a fuel

for the cells (and as such needs oxygen for oxidation),

it is very important in diabetes to get a handle on car-

bohydrate intake (and therefore glucose). There are

foods that slowly release glucose from the polysac-

charides, e.g. Rice, which is so slowly absorbed into

the circulation that the insulin release may still be suf-

ficient, or those that are quickly broken down into

glucose, e.g. White bread, which overwhelms insulin

production and release. The glycemic index of carbo-

hydrates indicates how quickly this conversion of car-

bohydrate to glucose takes place. It is not clear from

the information how the food is composed and how

much the study participants ingest. Only the disease

with characteristics was associated, not the healthy

status. It can therefore be expected that those traits re-

sult in a rule that are connected to the carbohydrate /

glucose metabolism.

Rule 1: In the case of type 1 diabetics, a balanced diet

can be expected, in the case of type 2 diabetics an in-

creased food intake. The type 1 diabetics, which are

obviously at issue here, consciously eat a balanced

supply of glucose, do not need any supplements and

do not smoke. If it were type 2 diabetics, they would

have ingested too much food and developed excess

weight in the past, which results in insidious type 2

diabetes with the dreaded complication "metabolic

syndrome". These type 2 diabetics are not always

conscious about their diet. Food supplements (vita-

mins, trace elements) would only have added value if

they were malnourished. This group shows no short-

age of micronutrients.

Rule 2: Compared to glucose intake, smoking has lit-

tle relevance for the measurement metabolism of glu-

cose. The prognosis can only worsen in the case of

consuming diseases. This group feeds irregularly,

therefore less consciously and accepts the dangers of

smoking. It could be type 2 diabetic.

Rule 3: Like rule 1, but with micronutrient intake,

perhaps diabetics with a less stable metabolism, more

frequent tiredness and weakness, which can be influ-

enced favourably with micronutrients.

8.3 Cholesterol

Hypercholesterolemia is a disease of the fat metabo-

lism; cholesterol can be biosynthesized purely inter-

nally. A chain is created from unused glucose, or its

degradation product acetyl-CoA, which ends with

cholesterol. Therapeutically, this synthetic route can

be interrupted with statins. The second possibility of

hypercholesterolemia is based on increased external

intake (high-fat diet, especially animal fats). In this

group, associations of sick and non-sick people with

the same characteristics are examined.

Rule 1: This group with a normal cholesterol level

eats a lot of vegetables, which also contains the nec-

essary micronutrients. If bread or other carbohydrate

are not consumed excessively, endogenous choles-

terol production remains low. The previous smoking

apparently did not cause any vascular changes, which

combined with hypercholesterolemia would worsen.

Rule 2: This group with normal cholesterol eats like

the group in rule 1 but does not smoke. Vascular walls

altered by atherosclerosis due to nicotine consump-

tion can be excluded. There is no cardiovascular risk.

Rule 3: This group with a normal cholesterol level is

not very conscious and, in combination with smoking,

has an increased risk of atherosclerosis, especially if

a lot of bread is eaten with butter instead of warm

meals.

Rule 4: This group with a normal cholesterol level

eats similarly to the group in Rule 3. The risk of hy-

percholesterolemia is reduced here by regular vegeta-

ble intake.

Rule 5: This group suffers from hypercholesterole-

mia. The profile is like the group in rule 4. However,

HEALTHINF 2021 - 14th International Conference on Health Informatics

the vegetable consumption is insufficient or has

started too late or cholesterol arises from too much

carbohydrate intake.

Rule 6: This group suffers from hypercholesterole-

mia. The profile is like the group in rule 5. The intake

of regular hot meals with a (hopefully) balanced com-

position and the non-smoking behaviour significantly

reduce the risk of atherosclerosis. The vessel walls

should be less changed here.

Rule 7: Like group in Rule 6, but with nutritional sup-

plements. These can be helpful if cholesterol espe-

cially emerged from internal biosynthesis. This form

would be amenable to therapy with statins. If you eat

too greasy, the risk can also be improved by adapting

the meal composition

9 CONCLUSION AND FUTURE

WORK

In this paper, we apply a data mining method such as

A-priori algorithm to a big integrated Swiss nutrition

and health database to gain rules that show the effects

of nutritional habits on some chronical diseases such

as high blood pressure, Diabetes and high Choles-

terol.

The interpretation of the derived rules reveals in-

teresting aspects about the selected Swiss population

subgroup. In general, the Swiss population nutritional

habits are reasonable in relation to chronical diseases.

The results show that the derived rules are only rele-

vant for a very small proportion of the sample.

Furthermore, the rules show that the appearance

of the mutually independent nutritional characteris-

tics in the various forms occurs in the rules equally

distributed which can be interpreted that most of the

sample population follow the state-of the art nutri-

tional standards, smoke little and do physical activi-

ties regularly.

Nevertheless, a small percentage of the sample

show chronic illnesses due to unhealthy eating. In

further research, the focus should be on the targeted

selection of the characteristics, their categorization

and the consideration of the characteristics in context,

as this is crucial for the association analysis and the

later interpretation of the rules. The weighting of

characteristics should also be considered in further

studies so that characteristics with a small total pro-

portion in the population do not drop out early due to

the minimum support criterion by A-priori algorithm.

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