COPERNICUS - AN EXPERT SYSTEM SUPPORTING

DIFFERENTIAL DIAGNOSIS OF PATIENTS EXAMINED USING

THE MMPI TEST

An Index-rule Approach

Jerzy Gomuła

The Andropause Institute, Medan Foundation, Warsaw, Poland

Cardinal Stefan Wyszyski University in Warsaw, Warsaw, Poland

Krzysztof Pancerz, Jarosław Szkoła

Institute of Biomedical Informatics, University of Information Technology and Management in Rzesz

ow, Rzesz

ow, Poland

Keywords:

Diagnostic decision-support system Copernicus, Mental disorders, MMPI, Logical decision rules, Differential

diagnosis.

Abstract:

In the paper, we present the Copernicus system - a computer tool supporting differential diagnosis of patients

with mental disorders. As an example, we discuss results for a sample of 479 women examined by means of

the MMPI-WISKAD personality inventory. There have been used the inductive classiﬁers based on the C4.5

decision tree algorithm. We have accomplished an overview of indexes generated on the basis of examined

sample by several systems: Eichmann’s, Diamond’s, Goldberg’s, Taulbee-Sisson’s, and Panda-APAP (mixed

with respect to indexes).

1 INTRODUCTION

One of the main tasks of building expert systems is

to search for efﬁcient methods of classiﬁcation of

new cases. Our research concerns psychometric data

coming from the Minnesota Multiphasic Personal-

ity Inventory (MMPI) test (Lachar, 1974). MMPI

is used to count the personality-psychometric dimen-

sions which help in diagnosis of mental diseases.

In years 1998-1999 a team of researchers consist-

ing of W. Duch, T. Kucharski, J. Gomuła, R. Adam-

czak created two independent rule systems devised

for the nosological diagnosis of persons that may be

screened with the MMPI-WISKAD test (Duch et al.,

1999). Testing some algorithms for the rule genera-

tion from the MMPI data was described in (Gomuła

et al., 2010a), (Gomuła et al., 2010c). Our research is

focused on creating a new computer tool for multicri-

teria diagnosis of mental diseases. The ﬁrst version of

this tool has been presented in (Gomuła et al., 2010b).

The Minnesota Multiphasic Personality Inventory

(MMPI) test (Lachar, 1974) delivers psychometric

data on patients with selected mental disorders. Orig-

inally, the MMPI test was developed and published

in 1943 by a psychologist S.R. McKinley and a neu-

ropsychiatrist J.Ch. Hathaway from the University of

Minnesota. Later the inventory was adapted in above

ﬁfty countries. The MMPI-WISKAD personality in-

ventory is the Polish adaptation of the American in-

ventory. It has been used, among other modern tools,

for carrying out nosological differential diagnosis.

MMPI is also commonly used in scientiﬁc research.

The test is based upon the empirical approach and

originally was translated by M. Chojnowski (as WIO)

(Choynowski, 1964) and elaborated by Z. Płu

zek (as

WISKAD) in 1950 (Płu

zek, 1971). American norms

were accepted there. On the basis of the received

responses (”Yes”, ”Cannot Say”, ”No”) to selected

questions we may obtain the reference and clinical

scales as being directly related to speciﬁc questions

(items) and recalculate the outcome into T-scores re-

sults. The T-scores ([T]) scale, which is traditionally

attributed to the MMPI, represents the following pa-

rameters: offset ranging from 0 to 100 T-scores, av-

erage equal to 50 T-scores, standard deviation equal

to 10 T-scores. The proﬁle that is built for such a

case always has a ﬁxed and invariable order of its con-

stituents as distributed on the scales. The validity part

323

Gomuła J., Pancerz K. and Szkoła J..

COPERNICUS - AN EXPERT SYSTEM SUPPORTING DIFFERENTIAL DIAGNOSIS OF PATIENTS EXAMINED USING THE MMPI TEST - An

Index-rule Approach.

DOI: 10.5220/0003172503230328

In Proceedings of the International Conference on Health Informatics (HEALTHINF-2011), pages 323-328

ISBN: 978-989-8425-34-8

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

Figure 1: A graphical user interface of Copernicus.

consists of three scales: L - the scale of lying which is

sensitive to all false statements aiming at representing

ourselves in a better light, F - the scale which detects

atypical and deviational answers to all items in the

test, K - it examines self defensive mechanisms and it

detects subtler attempts of the subject being screened

at falsifying and aggravation. The clinical part con-

sists of ten scales: 1. Hypochondriasis (H p), 2. De-

pression (D), 3. Hysteria (Hy), 4. Psychopathic Devi-

ate (Ps), 5. Masculinity/Femininity (Mk), 6. Paranoia

(Pa), 7. Psychastenia (Pt), 8. Schizophrenia (Sc), 9.

Hypomania (Ma), 0. Social introversion (It).

2 THE COPERNICUS SYSTEM - A

GENERAL OVIERVIEW

The Copernicus system is a computer tool for mul-

ticriteria differential diagnosis of patients with men-

tal disorders. The tool was designed for the Java

platform. This makes the tool a modern platform-

independent, object-oriented, user-friendly applica-

tion. A graphical user interface is presented in Fig-

ure 1. The current version of this tool offers sev-

eral main functions. We can locate patients in a pro-

ﬁle space using a wide variety of measures and in-

dexes (e.g., general distance measures, specialized

measures, psychopathology indexes). We can match

patient proﬁles to patterns of disorders using dendro-

grams generated by different clustering methods with

a suitable visualization. In a tool, two approaches

to generating dendrograms (Gan et al., 2007) are ap-

plied: standard hierarchical clustering techniques as

well as an approach based on the so-called Wroclaw

taxonomy. We can visualize patient proﬁles on the

background of patterns of disorders as well as de-

cision rules generated by popular data mining sys-

tems. An important thing is a unique visualization

of decision rules (in the form of stripes put on pro-

ﬁles) supporting the nosological diagnosis. We can

characterize patients by a number of indexes of the

MMPI systems (Dahlstrom et al., 1986), (Kucharski

and Gomuła, 1998b), (Kucharski and Gomuła, 1998a)

like Diamond’s, Leary’s, Goldberg’s, Eichmann’s,

Panda-APAP, and some other important and useful in-

dexes. In the case of the Diamond’s and Leary’s sys-

tems, indexes are arranged in the form of appropriate

diagrams (a box diagram for the Diamond’s system

and an radar-angular diagram for the Leary’s system)

which enable the user to interpret a patient proﬁle in a

graphical way. The design and implementation of the

presented tool take into consideration its modularity.

Therefore, the tool can easily be extended to other in-

telligent methods used in data mining and analysis, as

well as, to other kinds of data, for example, coming

from another inventories.

3 INPUT AND OUTPUT DATA

We have examined 479 clinical cases (women).

A sample was selected using the competent judge

method (the majority of two-thirds of votes of three

clinicians-diagnosticians with several years’ experi-

ence in clinical diagnosis using the MMPI-WISKAD

test). Each case is classiﬁed to one of ﬁve psychi-

atric nosological types: neurosis (neur), psychopathy

(psych), schizophrenia (schiz), simulation (simu), dis-

simulation (dissimu) as well as to norm (norm). Data

vectors can be represented in a graphical form as the

so-called MMPI proﬁles. The proﬁle has always a

ﬁxed and invariable order of its constituents (scales).

In the Copernicus system, various procedures

have been implemented, i.e., procedures plotting

scale and index proﬁles, box diagrams, radar-angular

diagrams as well as the basis of classiﬁcation (deci-

sion) rules and their striped visualization decidedly

improving differential diagnosis; the basis of noso-

logical patterns for different genders (separately for

men, separately for women) and classiﬁcation (de-

cision) rules also with respect to the examined gen-

der; superimposing rules on patterns; visualization of

classiﬁcation functions. The classiﬁcation functions

(Cios et al., 2007) are deﬁnitely better and more pre-

cise than the rough Goldberg’s indexes and they con-

cern not four but twenty nosological classes (also with

respect to gender of the examined patient). Ranges

of the Goldberg’s indexes, determined by us, conﬁrm

their high diagnostic relevance also for the MMPI-

WISKAD data (examined women). Many clinicians

feel an attachment to these indexes. Therefore, we

have used an index-rule approach to the Goldberg’s

indexes which enables us to determine values of their

ranges and classiﬁcation accuracy of selected classes.

In the Copernicus system, there have been im-

HEALTHINF 2011 - International Conference on Health Informatics

324

plemented numerous matching and distance measures

enabling the user to inspect a degree of matching and

consistency of a given proﬁle with a nosological pat-

tern or norm (see (Gomuła et al., 2010b)). An exam-

ined proﬁle consists of scales. There is possibility of

excluding any scales like in a clinical practice. The

proﬁle can be extended by various indexes which are

calculated, added to the proﬁle, and displayed in dif-

ferent ways (tabular collation, diagrams).

4 EXPERIMENTS

In our experiments, the Copernicus and WEKA sys-

tems have been used. WEKA is a collection of ma-

chine learning algorithms for data mining tasks (Wit-

ten and Frank, 2005). The main goal of experiments

is generation of efﬁcient classiﬁcation (decision) rules

via decision trees on the basis of proﬁles of patients

and selected indexes calculated for proﬁles. Indexes

added to proﬁles (scales) have been calculated using

the Copernicus system. Next, for decision tree gener-

ation, the well-known C4.5 algorithm (Quinlan, 1993)

(implemented in WEKA) has been used. To avoid ex-

cessive speciﬁcity (overﬁtting) of a decision tree and

to improve its prediction value the standard approach,

called pruning the decision tree, has been used. The

complete decision tree has been generated, and next

non-signiﬁcant branches have been removed (the so-

called post-pruning). If a decision tree is overﬁtted,

then it captures erroneous classiﬁcation information,

which will tend to make it perform badly when clas-

sifying unseen cases. In the subsections, we present

several experiments performed for proﬁles (scales)

and selected indexes.

4.1 Classiﬁcation Rules only from

Proﬁles (Validity and Clinical

Scales)

There is some problem with the 5.(M f ) scale. A fea-

ture selection analysis using several methods imple-

mented in the WEKA system, for example, Signif-

icanceAttributeEval together with Ranker, Genetic-

Search together with CfsSubsetEval conﬁrms this fact

(see Table 1). The SigniﬁcanceAttributeEval method

shows that scales: 8.(Sc), 6.(Pa), 4.(Ps), 3.(Hy),

0.(It), 7.(Pt), 1.(H p), F, and 2.(D) are very impor-

tant in the rank whereas scales K and L are the weak-

est ones and they need psychometric revision (which

happened in the MMPI-2 test). Scale 9 needs reﬁn-

ing (also in view of speciﬁcity and difﬁculty of test

examination of maniacal states/episodes). For a deci-

Figure 2: Visualization of proﬁles and rules.

sion tree generated using the C4.5 algorithm, we ob-

tain that only ten rules are sufﬁcient to make clini-

cal differential diagnosis (see Table 3(a)) in the scope

of ﬁve most important nosological classes and norm

with accuracy 85% and greater. The average accuracy

is 88%. Arrows indicate lower and upper bounds of a

rule. Exemplary visualization of proﬁles and rules (in

the form of stripes put on proﬁles) is shown in Figure

2. If all conditions of a given rule cut across the pro-

ﬁle, then the rule is satisﬁed and diagnosis indicated

by a rule can be assigned to the proﬁle.

Table 1: Attribute selection using (a) SigniﬁcanceAttribu-

teEval together with Ranker, (b) GeneticSearch together

with CfsSubsetEval.

Avg merit Avg rank Attribute

0.959 ± 0.016 1 ± 0 8.(Sc)

0.9 ± 0.015 2.4 ± 0.92 6.(Pa)

0.878 ± 0.013 2.8 ± 0.4 4.(Ps)

0.853 ± 0.007 4.6 ± 0.92 3.(Hy)

0.854 ± 0.012 4.7 ± 1 0.(It)

0.841 ± 0.01 6.2 ± 0.87 7.(Pt)

0.84 ± 0.005 6.3 ± 0.64 1.(H p)

0.813 ± 0.008 8 ± 0 F

0.781 ± 0.012 9 ± 0 2.(D)

0.741 ± 0.028 10 ± 0 9.(Ma)

0.676 ± 0.008 11 ± 0 K

0.607 ± 0.023 12.1 ± 0.3 L

0.558 ± 0.013 12.9 ± 0.3 5.(Mk)

Number Attribute

folds

(%)

100 L

100 F

100 K

100 1.(H p)

100 2.(D)

100 3.(Hy)

100 4.(Ps)

90 5.(Mk)

100 6.(Pa)

100 7.(Pt)

100 8.(Sc)

90 9.(Ma)

100 0.(It)

The 5.(Mk) scale was excepted. A set of rules ob-

tained from proﬁles after the transformation of a deci-

sion tree is shown in Table 3(a). Rules have the form

of logical implications: IF conjunction of conditions

(concerning values of scales) is satisﬁed, THEN deci-

sion (nosological class) should be taken with a given

accuracy (certainty), for example, IF L <= 58[T ]

AND 1.(H p) <= 57[T ], THEN R1 : norm (with ac-

curacy 89%). All classiﬁcation (decision) rules from

our set (see all tables with rules) have a separable

(hence differential) character. Separability is assured

by the inequality test applied in nodes of a decision

COPERNICUS - AN EXPERT SYSTEM SUPPORTING DIFFERENTIAL DIAGNOSIS OF PATIENTS EXAMINED

USING THE MMPI TEST - An Index-rule Approach

325

tree: a <= v, where a is an attribute (scale, index),

v is a threshold for which the criterion of splitting is

maximized. While using rule-based differential diag-

nosis, narrative descriptions can be restricted to scales

and their ranges occurring in conditions of the satis-

ﬁed rule. Such narrative based on satisﬁed and closed

decision rules can be called a narrative diagnosis of

the ﬁrst level. As it is known, the appropriate clinical

descriptions correspond to individual scales. Rules

can be also closed by minimal or maximal value, re-

spectively, for a given scale in a given class (or for all

classes). An example is shown in Table 2. A rule is

closed by minimal and maximal values of scales for

the schiz class. There is some paradox. A process of

Table 2: An example of closing a rule.

1.(H p) 6.(Pa) 9.(Ma) Rule No.:class (accuracy %)

> 64 <= 77 > 56 R6:schiz (85%) - before closing

65 − 89 47 − 77 57 − 85 R6:schiz (85%) - after closing

closing conditions of rules can lead to smaller predic-

tion values of rules, but it can increase the narrow-

range accuracy of a narrative description.

4.2 Classiﬁcation Rules from Proﬁles

and Eichmann’s Indexes

For each pair of different scales, the Eichmann’s in-

dexes are calculated as a sum, a difference, and an

arithmetic average of two scales, for example L − F,

L + F,

L−F

, ..., L − 1.(H p), L + 1.(H p),

L−1.(H p)

etc. Originally, the Eichman’s system included ﬁve

components (the so-called L components, K compo-

nents, F components, M f components, and NP com-

ponents). A component is a scale subtracted from

another scale. In our approach, we extend the Eich-

mann’s indexes to all twelve components (subtraction

of the same scales is omitted). All indexes were cal-

culated only in [T] for the whole sample of women.

Classiﬁcation rules obtained from data consisting of

proﬁles and the Eichmann’s indexes are shown in Ta-

ble 3(b). The average accuracy is 88.9%. We can

assign clinical rational interpretation to the selected

Eichmann’s indexes, for example, D − Ma is the ac-

tivity (Diamond’s) or mood disturbance index. The

Eichmann’s indexes indicated by a decision tree ques-

tion credibility of code type systems. Not only the

height of the highest scales (clinical, >= 70[T ] or

>= 65[T ]) in a proﬁle constituting a given code type

is important, but also sums and differences between

selected scales. It concerns both validity (L, F, K)

and clinical scales. All obtained rules can be treated

as new code types with the determined accuracy.

Figure 3: A hierarchy of classes for Goldberg’s indexes.

Figure 4: An exemplary Leary’s diagram.

4.3 Classiﬁcation Rules from Proﬁles

and Goldberg’s Indexes

The Copernicus system enables the user to calculate

three Goldberg’s indexes. The ranges of the Gold-

berg’s indexes have been determined by rules for each

of three levels (see Figure 3). Simulation (simu) and

dissimulation (dissimu) have been excluded from the

analysis. A macroclass of deviational proﬁles in-

cludes three classes: neurosis (neur), schizophrenia

(schiz), and psychopathy (psych). A macroclass of

psychiatric includes neurosis and schizophrenia. A

class of sociopathy is formed by its counterpart, i.e.,

psychopathy. The Goldberg’s indexes have an excel-

lent property differentiating at each level with accu-

racy at least 91%. Classiﬁcation rules obtained from

data consisting of proﬁles and the Goldberg’s indexes

are shown in Table 4(a).

4.4 Classiﬁcation Rules from Leary’s

Indexes

The Copernicus system enables the user to calculate

the Leary’s indexes (Leary, 1957). Indexes can also

be visualized as it is shown in Figure 4. For de-

cision rule generation, all of 497 proﬁles have been

used. For each proﬁle, eight Leary’s indexes (called

styles) are calculated: (1) Managerial - Autocratic

style M − A, (2) Responsible - HyperNormal style

R − H, (3) Cooperative-Over - Conventional style

C −C, (4) Docile - Dependent style D − D, (5) Self-

Effacing - Masochistic style E − M, (6) Rebellious

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Table 3: (a) Rules obtained after transformation of a decision tree generated for all scales excluding scale 5, (b) Rules obtained

for proﬁles and Eichmann’s indexes, (c) Rules obtained for proﬁles and all indexes.

L 1.(H p) 3.(Hy) 6.(Pa) 8.(Sc) 9.(Ma) 0.(It) Rule No.:class (accuracy %)

<= 58 <= 57 R1:norm (89%)

58 − 64 <= 59 <= 77 <= 68 R2:norm (89%)

> 64 <= 77 <= 56 R3:neur (85%)

58 − 64 > 59 <= 77 <= 68 R4:neur (85%)

> 58 <= 57 > 58 R5:psych (92%)

> 64 <= 77 > 56 R6:schiz (85%)

58 − 64 <= 77 > 68 R7:schiz (85%)

> 58 <= 57 <= 58 > 59 R8:simu (94%)

> 57 > 77 R9:simu (94%)

> 58 <= 57 <= 58 <= 59 R10:dissimu(85%)

Hy + It L + Ps F D − Hy Pt − Ma F + Ps H y − Ma H p + D L + Mk D − Ma K − Pt Rule No.:class (accuracy %)

> 119 <= 157 <= 13 <= 123 > 117 R1:simu (94.4%)

> 119 <= 157 <= 13 <= 123 <= 117 R2:norm (88%)

<= 119 <= 125 <= 58 R3:dissimu (70%)

<= 119 <= 125 > 58 R4:norm (88%)

<= 119 > 125 <= 5 R5:dissimu (70%)

<= 119 > 125 > 5 <= 11 R6:psych (94%)

<= 119 > 125 > 5 <= 11 R7:schiz (85.1%)

> 119 > 125 > 20 > 157 R8:neur (89.1%)

> 119 <= 20 > 157 R9:simu (94.4%)

> 119 <= 157 > 13 R10:neur (89.1%)

> 119 <= 157 <= 13 > 123 > 8 > −27 R11:schiz (85.1%)

> 119 <= 157 <= 13 > 123 > 8 <= −27 R12:neur (89.1%)

SocAgg/ avgDrInt 1.M − A avgHost SocInt 0.(It) Rule No.:class (accuracy %)

<= 1 <= 67 <= −5 > −12 R1:norm (96.0%)

> 1 > 67 <= 73 <= −12 R2:neur (93.1%)

<= 1 > 67 <= 73 R3:schiz 93.1%)

<= 1 <= 67 <= −5 R4:psych (96.8%)

<= 1 > 67 <= −5 > 73 R5:simu 87.9%)

<= 1 <= 67 > −5 > 55 R6:simu 87.9%)

<= 1 <= 67 > −5 <= 55 R7:dissimu (95.0%)

- Distrustful style R − D, (7) Aggressive - Sadistic

style A−S, (8) Competitive - Narcissistic style C − N.

After joining a proﬁle (validity and clinical part ex-

cluding the 5.(Mk) scale) with the Leary’s indexes

and next generating decision rules, we came with the

following conclusion: the best clinical differentiation

is for styles 1.M − A, 4.D − D, and 7.A − S. Two

Leary’s indexes determining two basic personality di-

mensions are not covered by a classiﬁer and they are

not used in classiﬁcation. Clinical scale 4.(Ps) rein-

forces the accuracy of differential diagnosis of psy-

chopathy to 60% and the average accuracy for the

whole classiﬁer to 90%. Especially, this scale dif-

ferentiates well between norm and psychopathy for

women. Classiﬁcation rules obtained from data con-

sisting of proﬁles and the Leary’s indexes are shown

in Table 4(b).

4.5 Classiﬁcation Rules from Proﬁles

and All Indexes

In the last experiment, rules have been induced for

all system indexes (Diamond’s, Leary’s, Goldberg’s,

and PANDA-APAP (Pancheri et al., 1992)) and addi-

tional indexes important diagnostically. The PANDA

system includes also the Taulbee-Sisson’s system as

a subsystem. The Taulbee-Sisson’s system differenti-

ates between neuroticism and psychoticism proﬁles.

Table 4: (a) Rules obtained for proﬁles and Goldberg’s in-

dexes, (b) Rules obtained for Leary’s indexes (without va-

lidity and clinical scales).

G1 G2 G3 Rule No.:class (accuracy %)

<= 137 <= 63 R1:norm (94.0%)

> 120 > 63 R2:psych (100.0%)

> 137 <= 63 R3:neur (85.1%)

<= 120 > 63 R4:schiz (94.1%)

1.M − A 4.D − D 7.A − S Rule No.:class (accuracy %)

<= −5 <= 192 <= 184 R1:norm (88%)

<= −42 > 192 <= 214 R2:neur (85%)

<= −5 <= 192 > 184 R3:psych (60%)

> −42 > 192 R4:schiz (87%)

> 192 > 214 R5:simu (95%)

> −5 <= 192 R6:dissimu (95%)

Copernicus counts the conditional Taulbee-Sisson’s

indexes and next indicates neuroticism (if at least thir-

teen indexes are satisﬁed), psychoticism (if at most

six indexes are satisﬁed), or an undeﬁned state (oth-

erwise). Rules have also been generated for proﬁles

and all listed indexes. Classiﬁcation rules obtained

from data consisting of proﬁles and all listed indexes

are shown in Table 3(c). The meaning of indexes is

as follows: SocAgg - social aggression (Diamond’s

system), avgDrInt - average dreams intellectualiza-

tion (Diamond’s system), avgHost - average hostil-

ity (Diamond’s system), SocInt - social introversion

(PANDA system). In this experiment, the strongly ex-

tended Eichmann’s system has been omitted. Its rule

analysis was performed in Subsection 4.2.

COPERNICUS - AN EXPERT SYSTEM SUPPORTING DIFFERENTIAL DIAGNOSIS OF PATIENTS EXAMINED

USING THE MMPI TEST - An Index-rule Approach

327

4.6 Summarry

Rule classiﬁcation trees enable the user to reduce in-

terdependent, superﬂuous and strongly outlying data.

Moreover, rule trees obtained by us have the ”white-

box” character, i.e., the knowledge representation is

overt, readable and useful for diagnosticians and clin-

icians. Rule knowledge-based systems are small and

do not exceed ﬁfteen classiﬁcation (decision) rules.

The greatest number (twelve) of rules was generated

for the Eichmann’s system. All our analyses demon-

strate that in an index-rule approach the selected in-

dexes, Eichman’s (calculated in [T]), extended Di-

amond’s, Leary’s (without two basic personality di-

mensions) are used not only for supporting diagnosis

in the so-called interpretation quiet (mostly the range

40 - 60 [T]) or only for delivering additional inter-

pretation (descriptive) hypotheses. Indexes selected

by us and additionally transformed into the index-rule

form give the high accuracy (above 80%). Therefore,

they can not be overlooked. This is their new clini-

cal and psychometric application. The systems men-

tioned earlier are implemented in Copernicus and they

support quantitative and clinical differential diagnosis

based on an index-rule approach.

5 CONCLUSIONS AND FURTHER

WORK

In the paper we have shown an index-rule-based ap-

proach to differential diagnosis of patients with men-

tal disorders. In our experiments we have used the

Copernicus system and the WEKA system. The

Copernicus system is a tool created by our team.

Among others, it enables the user to calculate a wide

variety of indexes used in diagnosis of psychopatholo-

gies. Some of them can also be visualized by means

of appropriate diagrams. WEKA is a collection of

machine learning algorithms for data mining tasks.

Our experiments deliver important information for

diagnosticians and clinicians about individual scales

(their validities and utilities) used in the MMPI test-

ing of patients. In the future work, among others, we

are going to incorporate rule generation algorithms

and visualization modules into the Copernicus sys-

tem. Our main goal is to deliver to diagnosticians and

clinicians an integrated tool supporting the compre-

hensive diagnosis of patients. The Copernicus system

is ﬂexible and it is dedicated for supporting differen-

tial diagnosis of proﬁles of patients examined using

multiphasic personality inventories.

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