Model for Quality of Life Evaluation of Countries European Union

with using Rule-based Systems

Martin Šanda and Jiří Křupka

Institute of System Engineering and Informatics, Faculty of Public Administration, University of Pardubice,

Studentská 84, Pardubice, Czech Republic

Keywords: AHP, EU, Fuzzy Sets, Fuzzy Inference System, Quality of Life Evaluation, Rule-based Systems, TOPSIS.

Abstract: This paper deals with the quality of life (QL) evaluation of countries European Union (EU) and progress of

this evaluation in years 2007, 2011 and 2015. QL evaluation is based on official Eurostat methodology for

QL evaluation - QL indicators for the EU, the data presented here come from several sources from within

the European Statistical System (ESS). The set of indicators is organised along the areas: Material living

conditions, Productive or main activity, Health, Education, Economic and physical safety, Governance and

basic rights and Natural and living environment. QL is evaluated with using rule-based systems method:

Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) - modification fuzzy TOPSIS

fuzzy inference system (FIS) and Analytic hierarchy process (AHP). The aim of this paper is creating model

for QL evaluation with using these methods, comparing results of these methods and their progression.

Result of model is final recommendation to reach the grant allocation for the countries or regional

development.

1 INTRODUCTION

Defining term QL brings dilemmas and each author

or institution has own approach and own solving this

problematic. If we occupy ourselves with defining

QL term, we have to consider influence of historical,

cultural and social changes, which take place in

given society.

The definition aptly describes the expert

discussions (Royuela et al., 2010), which state that

QL: “usually refers to the degree to which a person’s

life is desirable versus undesirable, often with an

emphasis on external components, such as

environmental factors and income. In contrast to

subjective well-being, which is based on subjective

experience, quality of life is often expressed as more

objective and describes the circumstances of a

person’s life rather than his or her reaction to those

circumstances.”

Among some common traits (Andráško, 2016)

which are typical for the issue of the QL research

also belongs a fragmentation of definitions, an

approach to the evaluation as well as

multidisciplinary and multidimensionality. The term

QL refers (Rapley, 2003) to human existence,

comprehension of meaning of life itself of individual

being. QL can be observed through two variables –

material and non-material part of human life and

includes individual way of life, not only individual

living conditions, but also living conditions of wider

groups of society as a whole. Model of QL (Rapley,

2003) is in Figure 1.

Figure 1: Model of quality of life.

QL should be looked (Curtis et al., 2002;

Phillips, 2006) upon as a multidimensional variable,

which contains information about psychosocial

status of an individual which is influenced by, for

example, age, gender, education, social status,

Šanda, M. and K

rupka, J.

Model for Quality of Life Evaluation of Countries European Union with using Rule-based Systems.

DOI: 10.5220/0006476604730479

In Proceedings of the 12th International Conference on Software Technologies (ICSOFT 2017), pages 473-479

ISBN: 978-989-758-262-2

473

economical situation or individual’s values. QL can

be viewed as availability of options, from which an

individual can pick during filling his life.

2 QUALITY OF LIFE

EVALUATION

The QL evaluation is a difficult thing and exist a lot

opinions and approaches. QL is evaluated by the

using of indicators. Individual indicators then form a

set of indicators or the whole methodology for

evaluating the QL. As examples of methodologies

(approaches) of QL evaluation we can quote: Active

Ageing Index (AAI, 2015); Economist Intelligence

Unit Limited (EIU, 2015); Eurofound (EF, 2015);

Better Life Index (OECD,2015).

The Eurostat official methodology was selected

for the created model. This methodology comprised

nine areas for QL evaluation and for evaluation were

selected indicators in years 2007, 2011, 2015. These

years have been selected due to the availability of

data and the trend of evaluation of the individual

countries. In this paper and in this model will be

evaluated countries, that have become members of

the EU in 2004 - Czech Republic, Estonia, Cyprus,

Latvia, Lithuania, Hungary, Malta, Poland, Slovenia

and Slovakia with compared to the EU-wide

average.

The created model will then be applicable to

other states or other selections. Unfortunately, only

27 pointers from seven areas were available for

evaluation, so this model will work with this

number. In the case of availability of data for

multiple indicators, the model could be expanded

(more indicators and areas). List of indicators is

described by Eurostat (2017): area of indicators

(area): indicators (unit) - sign.

 Material living conditions (area A): indicator

Mean and median income (unit Euro) - K1, At-

risk-of-poverty rate (% of total population) - K2,

S80/S20 income quintile share ratio (quotient) -

K3, Actual individual consumption per capita

(Nominal expenditure per inhabitant in Euro) -

K4, Severely materially deprived people (% of

total population) - K5, (In)ability to make ends

meet (% of total population) - K6, Share of total

population living in a dwelling with a leaking

roof, damp walls, floors or foundation, or rot in

window frames of floor (% of total population) -

K7, Overcrowding rate (% of total population) -

K8, Share of people living in under-occupied

dwellings (% of total population) - K9,

 Productive or main activity (area B):

Unemployment rate (%) - K10, People living in

households with very low work intensity (% of

total population aged less than 60) - K11,

Average number of usual weekly hours of work

in main job by economic activity (hour) - K12,

Population in employment working during

unsocial hours - nights (%) - K13, Temporary

contracts (%) - K14,

 Health (area C): Self-perceived health, good and

very good (%) - K15, Self-reported unmet needs

for medical examination, too expensive or too far

to travel or waiting list (%) - K16,

 Education (area D): Education attainment,

tertiary education (% of total population) - K17,

Early leavers from education and training (% of

the population aged 18-24 with at most lower

secondary education and not in further education

or training) - K18, Individuals' level of Internet

skills (% of Individuals who completed at least 2

of the 6 internet-related activities) - K19, People

that participated in education or training in the

four preceding weeks (%) - K20,

 Economic and physical safety (area E):

Population unable to face unexpected financial

expenses (% of total population) - K21,

Population in arrears, debt (% of total

population) - K22, Crime, violence or vandalism

in the area (% of total population) - K23,

 Governance and basic rights (area F): Gender

employment gap (difference between the

employment rates of men/ women aged 20-64) -

K24, Gender pay gap in Industry, construction

and services, except public administration,

defense, compulsory social security (average

gross hourly earnings of male/female paid

employees as a % of average gross hourly

earnings of male paid employees) - K25,

 Natural and living environment (area G):

Pollution, grime or other environmental

problems (% of total population) - K26, Noise

from neighbours or from the street (% of total

population) - K27.

3 MODEL FOR EVALUATION

As described in the previous section, 27 indicators

from seven areas of the Eurostat official

methodology from 2007, 2011 and 2015 were

selected for the QL evaluation.

ICSOFT 2017 - 12th International Conference on Software Technologies

474

3.1 Describe of Model

For QL evaluation has proven to be a beneficial use

of system engineering methods (for example Šanda

and Mandys, 2017; Šanda and Křupka, 2016;

Křupka et al., 2010; Kačmárová et al., 2013) among

which, among other things, are the methods of

multi-criteria decision making, rule-based systems

and fuzzy logic.

The combination of these methods was been

used to solve problems and creating model. In model

were used TOPSIS method (respectively its fuzzy

modification) and FIS for solving problem and QL

evaluation. Subsequently, the AHP method was

used to compare the ranking results between the

methods. The model then worked with defined fuzzy

sets (FSs) too. The general scheme of the model is in

Figure 2.

Figure 2: Model for quality of life evaluation.

In the previous text were described the issue of

QL assessment, data source and selected indicators.

The following will be described methods, which

have been used to solve the problem - "core" of the

model.

3.2 Fuzzy Sets

Fuzzy logic was also used for the solution - fuzzy

sets were defined for QL evaluation. Based on

previous work in this field QL were defined 4 fuzzy

sets for area evaluation and 5 FS for total QL

evaluation - described below. In this article the

intervals of FS were specified and there were used

FS of trapezoidal shape of MF in the form [a b c d]

Mathworks (2017), where parameters ‘a’ and ‘d’

locate the ’feet’ of the trapezoid and the parameters

‘b’ and ‘c’ locate the ‘shoulders’.

Defined FS and their linguistic variables for

areas evaluation: very bad [0 0 0.4 0.45], bad [0.4

0.45 0.6 0.65], good [0.6 0.65 0.8 0.85], very good

[0.8 0.85 1 1.2]. A graphical image of the defined FS

is in the Figure 3.

Figure 3: Fuzzy sets for areas evaluation.

Defined FS and their linguistic variables for total

evaluation: very bad [0 0 0.4 0.45], bad [0.4 0.45 0.6

0.65], good [0.6 0.65 0.75 0.8], very good [0.75 0.8

0.9 0.95] and perfect [0.9 0.95 1 1]. A graphical

image of the defined FS is in the Figure 4.

Figure 4: Fuzzy sets for total evaluation.

3.3 Using Methods in Model

The model worked with methods TOPSIS and its

fuzzy modification, FIS and AHP.

TOPSIS method is according to Senouci et al.

(2016), Chen and Hwang (1992) one of the multi-

criterial decision algorithm, which is based on the

option selection. It is assumed that the maximization

character of all criteria (if all criteria are not

maximization, it is necessary to transform them).

TOPSIS ranks the subjects according to the score,

when the highest is the best resolution.

The basic rule is that, the preferred alternative

should have the shortest distance from the ideal

resolution and the longest distance from the negative

– the worst resolution. In the created model was

used the extension of TOPSIS - fuzzy TOPSIS,

Model for Quality of Life Evaluation of Countries European Union with using Rule-based Systems

475

where defined fuzzy sets were used. Weights of

indicators were solved with share (1/27).

General structure of FIS is used for the resolution

according to Zadeh (2015); Hu et al. (2017); Yang et

al. (2017); Bělohlávek et al. (2002) and Kang et al.

(2017). Before its own QL evaluation with FIS

usage, it is necessary to resolve: normalized matrix,

define the rules and fuzzy sets for the QL evaluation,

Mamdani type of FIS was used. Based on

experimental FIS settings (Šanda and Křupka,

2017), it was the optimal solving trapezoidal shape

of membership function (MF) and method Centre of

Gravity used in defuzzification. The number of rules

depends on the number of criteria in the individual

area (for area B is 5) and the number of defined FSs

(for areas 4), for area B it is 45, a total 1024 rules.

Examples of rules of area B:

 Rule

: If (K10 is very-bad) and (K11 is very-

bad) and (K12 is bad) and (K13 is very-good)

and (K14 is bad) then (QL-area-B is bad)

 Rule

907

: If (K10 is very-good) and (K11 is good)

and (K12 is very-bad) and (K13 is good) and

(K14 is good) then (QL-area-B is good).

Inputs to FIS-area are indicators (chapter 2), output

is QL evaluation of area; inputs to FIS-TOTAL are

outputs form FIS of areas, output is total QL

evaluation - see in Figures 5 and 6.

Figure 5: Hierarchy structure of FIS for QL-area-D.

Figure 6: Hierarchy structure of FIS QL-total evaluation.

AHP is (Dweiri, 2016) a multi-criteria decision

making method. It is developed by Saaty to assist in

solving complex decision problems by capturing

both subjective and objective evaluation measures.

AHP uses a pair-wise comparison of the criteria

importance with respect to the goal. This pair wise

comparison allows finding the relative weight of the

criteria with respect to the main goal. If quantitative

data is available, the comparisons can be easily

performed based on a defined scale or ratio and this

cause the inconsistency of the judgment will be

equal to zero which leads to perfect judgment. If

quantitative data is not available, a qualitative

judgment can be used for a pair wise comparison.

This qualitative pair wise comparison follows the

importance scale suggested by Saaty. The same

process of pair-wise comparison is used to find the

relative importance of the alternatives with respect

to each of the criteria. Each child has a local

(immediate) and global priority (weight) with

respect to the parent. The sum of priorities for all the

children of the parents must equal 1. The global

priority shows the alternatives relative importance

with respect to the main goal of the model. The pair-

wise comparison is performed in matrix format to

check the consistency of the judgment.

It breaks a complex problem into hierarchy or

levels as shown in Fig. 7.

Figure 7: Example AHP structure.

3.4 Extension of Model

One of the aims of this article is the recommendation

for the grant allocation or a grant for regional

development (e.g. for the region with long-lasting

bad results) and per cent value of the grant for the

selected region. This recommendation is based on

the EIU (2015), which is in the Table 1. In this

article this approach is modified namely percentage

(per cent amount) of from the "development"

operational program (which would be specially

created). It is then possible to specifically define the

area for the grant allocation from the partial results

of the QL evaluation of the single areas.

ICSOFT 2017 - 12th International Conference on Software Technologies

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Table 1: Suggestion for allowance.

QL evaluation rating (%) Suggested allowance (%)

80 - 100 0

70 - 80 5

60 - 70 10

50 - 60 15

50 or less 20

4 RESULTS

In the following tables 2 and 3 are the QL evaluation

results of using fuzzy TOPSIS and FIS.

Table 2: Results of fuzzy TOPSIS method.

Country 2007 2011 2015

EU 73.86% 60.56% 75.10%

Czech Republic 61.18% 61.86% 63.15%

Estonia 53.50% 59.20% 46.42%

Cyprus 67.83% 64.99% 65.67%

Latvia 59.88% 49.77% 62.21%

Lithuania 50.81% 52.96% 52.10%

Hungary 63.32% 55.58% 71.03%

Malta 50.93% 66.28% 53.26%

Poland 70.81% 59.20% 60.49%

Slovenia 57.34% 66.50% 62.56%

Slovakia 56.20% 60.56% 55.61%

The results show that, on the basis of selected

indicators, in the selected years, the Baltic States are

the worst, further partial unstable values are reported

in values Hungary and Poland. The Czech Republic,

Cyprus, Malta, Slovenia and Slovakia show a

positive trend relative to the EU average. The island

states, Slovenia and the Visegrad group can then be

labelled as states whose results are above the EU

average after accession. On the other hand, the

countries of the Baltic States have lagged behind the

EU average. The result can be explained by the fact

that it is the republics from Soviet Union.

Table 3: Results of FIS.

Country 2007 2011 2015

EU 52.50% 70.00% 74.61%

Czech Republic 58.34% 52.50% 76.38%

Estonia 52.50% 52.65% 70.00%

Cyprus 52.50% 70.00% 70.00%

Latvia 21.03% 52.50% 52.50%

Lithuania 52.50% 52.50% 52.50%

Hungary 52.50% 53.53% 52.50%

Malta 66.57% 70.00% 70.00%

Poland 52.50% 52.65% 52.50%

Slovenia 70.00% 76.24% 76.24%

Slovakia 52.50% 70.00% 76.24%

Differences between the fuzzy TOPSIS and FIS

were as follows - 2007: average 12,92% and median

12,66%; 2011: 5,91% and 6,55%; 2015: 11,75% and

13,22%.

The results of these methods were then compared

with the results of the AHP. The comparison showed

that the biggest differences were in all the Baltic

countries, partly in Hungary and Slovakia, rarely in

Malta and Slovenia. In general, it can be said from

the results that the larger differences were between

the FIS and AHP methods. Table 4 shows an

example of differences in ranking in 2011 (fT is

fuzzy TOPSIS).

Table 4: Comparison ranking between methods.

Country fT/AHP FIS/AHP fT/FIS

EU 1 2 3

Czech Republic 1 0 1

Estonia 1 1 2

Cyprus 2 1 1

Latvia 1 5 6

Lithuania 2 3 5

Hungary 2 7 5

Malta 0 0 0

Poland 2 4 2

Slovenia 2 2 0

Slovakia 2 3 3

5 CONCLUSIONS

On the basis of the results of the individual methods,

it is possible to compile tables with recommendation

for grant allocation, which should be directed to the

development of states or region. Tables 5, 6 and 7

are recommendations for individual years, the

"Average" column indicates the average

recommendation for grant allocation.

Table 5: Recommendation for the grant allocation 2007.

Country fTOPSIS FIS Average

Czech Republic 10.00% 15.00% 12.50%

Estonia 15.00% 15.00% 15.00%

Cyprus 10.00% 15.00% 12.50%

Latvia 15.00% 20.00% 17.50%

Lithuania 15.00% 15.00% 15.00%

Hungary 10.00% 15.00% 12.50%

Malta 15.00% 10.00% 12.50%

Poland 5.00% 15.00% 10.00%

Slovenia 15.00% 5.00% 10.00%

Slovakia 15.00% 15.00% 15.00%

Model for Quality of Life Evaluation of Countries European Union with using Rule-based Systems

477

Table 6: Recommendation for the grant allocation 2011.

Country fTOPSIS FIS Average

Czech Republic 10.00% 15.00% 12.50%

Estonia 15.00% 15.00% 15.00%

Cyprus 10.00% 5.00% 7.50%

Latvia 20.00% 15.00% 17.50%

Lithuania 15.00% 15.00% 15.00%

Hungary 15.00% 15.00% 15.00%

Malta 10.00% 5.00% 7.50%

Poland 15.00% 15.00% 15.00%

Slovenia 10.00% 5.00% 7.50%

Slovakia 10.00% 5.00% 7.50%

From these two tables, it is clear that the highest

recommendation is for Baltic States. Higher is

recommendation partly in the Visegrad group.

On the contrary, Table 7 shows that the years are

gradually improving and the trend is positive, so the

overall recommendation is less range.

Table 7: Recommendation for the grant allocation 2015.

Country fTOPSIS FIS Average

Czech Republic 10.00% 5.00% 7.50%

Estonia 20.00% 5.00% 12.50%

Cyprus 10.00% 5.00% 7.50%

Latvia 10.00% 15.00% 12.50%

Lithuania 15.00% 15.00% 15.00%

Hungary 5.00% 15.00% 10.00%

Malta 15.00% 5.00% 10.00%

Poland 10.00% 15.00% 12.50%

Slovenia 10.00% 5.00% 7.50%

Slovakia 15.00% 5.00% 10.00%

The total results based on Tables 5, 6 and 7 are

then as follows: Latvia (recommendation for the

grant allocation is 15,83%), Lithuania (15%),

Estonia (14,17%), Hungary and Poland (12,5%),

Czech Republic and Slovakia (10,83%), Malta

(10%), Cyprus (9,17%) and Slovenia (8,33%). These

results again confirm the "lagging" of the Baltic

states, the good results of the island states and the

attractions are also the same result Czech Republic

and Slovakia (Czechoslovakia before year 1993).

If we take a closer look at the three groups -

"Island states" (Cyprus and Malta), the Visegrad

group (Czech Republic, Hungary, Poland and

Slovakia) and the Baltic States (Estonia, Lithuania

and Latvia), the recommendation for the Baltic

States is 15%, Visegrad group 11.7% and "Island

states" 9.6%. This grant recommendation for the

Baltic States that is one of the important

conclusions.

Recommendations for further work and

development of the model include the use of

multiple methods of system engineering, their

synthesis and analysis; using more criteria; the

inclusion of indicators for weights and areas;

availability of data (current disadvantage).

The created model for QL evaluation can then be

adjusted according to the number of available

criteria, supplemented by more (available) years and

applied to other states or groups of countries. The

model for QL evaluation can be used, for example,

for evaluating regions (NUTS2 or NUTS3) or the

like. The topic for further work is also to deal in

more detail with the recommendation for grant

allocation, the grant source, etc.

ACKNOWLEDGEMENTS

This article was supported by the projects No.

SGS_2017_019 of the Ministry of Education, Youth

and Sports of CR with title ‘Models Synthesis and

Analysis for Implementation Support of Smart Cities

and Regions Concept’ at the Faculty of Economics

and Administration, University of Pardubice.

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