DATA MINING APPLICATION TO OBTAIN PROFILES OF

PATIENTS WITH NEPHROLITHIASIS

Luis Zárate, Paulo Alvarenga, Romero Paoliello, Thiago Ribeiro

Applied Computational Intelligence Laboratory (LICAP)

Pontifical Catholic University of Minas Gerais (PUC)

Av. Dom José Gaspar, 500, Coração Eucarístico

Belo Horizonte, MG, Brasil, 30535-610

ywords: KDD, Data Mining, Discriminant Rules, Clinical Databases, Nephrolithiasis

Abstract: Nephrolithiasis is a disease that is unknown yet a clinical treatment that determines its cure. In the adult

population is esteemed an incidence around 5 to 12%, being a little lesser in the pediatric band. The renal

colic, caused by nephrolithiasis, is the main disease symptom in the adults and it is observed in 14% of the

pediatric patients. The disease symptoms in the pediatric patient don't follow a pattern, and this makes

difficult the disease diagnosis. The main objective of this work is discovery the patters of the disease

symptoms and identifies the population apt to acquire it. With this objective, is applied KDD methodology

determining discriminant rules for the patterns of the symptoms, and with this, select the groups of patients

with those sets of symptoms. Finally, the results and the conclusions of the work are presented.

1 INTRODUCTION

Nephrolithiasis is a disease that is unknown yet a

clinical treatment that determines its cure. In the

adult population is esteemed an incidence around 5

to 12%, being a little lesser in the pediatric band.

The renal colic, caused by nephrolithiasis, is the

main disease symptom in the adults and it is

observed in 14% of the pediatric patients. The

disease symptoms in the pediatric patient don't

follow a pattern, and this difficult the disease

diagnosis. The main objective of this work is

discovery the patterns of the disease symptoms and

identifies the apt population to acquire it.

The diagnosis of the disease is accomplished

through clinical exams and by the observation of the

symptoms. Between the main symptoms we can cite:

abdominal pain with not specific localization,

hipertension, fast and gasping breath, nauseas,

vomits, anorexy, indisposition, pulse and arterial

pressure with alteration, and hematuria macro or

microscopic. On the other hand, the diagnosis that

seems easy due to the intrinsic symptoms

characteristics, as vomit, abdominal pain, gasping

breath, among others, can take to a false diagnosis.

This phenomenon is understandable if we remember

that those symptoms are the same of other diseases,

as for example, appendicitis, acute pancreatitis, etc.

Therefore, exams of laboratory are necessary to

confirm the diagnosis. In this work, is discussed a

strategy to discovery the disease patterns on

symptoms proceeding from exams of laboratory,

genetic inheritance, and by the patient's clinical

observation.

The discovered patterns consider the presence or not

of the symptom in the patient and they will be

expressed through discriminant rules that can assist

in the disease diagnosis. On the other hand, it is also

important to know the groups profile of risk patients,

what can be obtained through techniques of

clustering, discrimination or classification. In this

work will be applied the KDD methodology based

on discriminant rules for discovery patters of disease

symptoms and for the identification of apt groups for

acquire it. The KDD Methodology (Fayyad, et. al.

1996 and Pyle, 1999) is a long procedure of specific

stages, having as only objective, the discovery of

knowledge in database. The KDD process involves

several stages, such as:

104

Zárate L., Alvarenga P., Paoliello R. and Ribeiro T. (2004).

DATA MINING APPLICATION TO OBTAIN PROFILES OF PATIENTS WITH NEPHROLITHIASIS.

In Proceedings of the Sixth International Conference on Enterprise Information Systems, pages 104-109

DOI: 10.5220/0002632501040109

 SciTePress

Symptom

Patiente

Symptom

Patiente

Data

Selection

Remove

Attribute

Delete Records

Transformation

of Attributes

Enhancement

Attributes

Remove

Outliers

Data Selection

Data Mining

Discriminant Rules

Knowledge

Figure 1: Applied KDD methodology

selection, enrichment, improvement, cleaning,

transformation, data mining and interpretation. The

application of KDD in the medical area has been

treated in several works, as can be seen in

Bojarezuk, Lopes & Freitas, 2001 and Werner &

Fogarty, 2001.

In this paper the diferents stages of KDD will be

applied in a nephrolithiasis database to obtain

discriminant rules that match with the disease

pattern in the patients. Those rules will make

possible verify the importance of the pattern, and the

symptoms description with the respective

occurrence. It is also objective of this work group

patients with the same profile to make possible the

identification of apt patients to develop the disease

through the discriminant rules.

This work is divided in 5 sections. In section 2, the

nephrolithiasis database, considered in this work, is

described. In section 3, the KDD process is applied,

and in the last sections the results and the

conclusions of the work are presented.

2 DESCRIPTION OF THE

CONSIDERED DATABASES

The space problem definition to be investigated

mentions the discovery patterns of disease

symptoms and the identification of apt groups to

acquire it. The database considered is composed by

patient’s data and diagnostic that had presented or

not the disease. This database presented 69 attributes

with the total of 363 records. The typical record of

the original database, with categorical names, is

express in (1).

},,,,,,{Record OLOCDGSCSLCCDP=

(1)

where: DP = personal data (23 attributes); CC =

clinical conduct (20 attributes); SL = symptoms

resulted by laboratory exams (12 attributes); SC =

clinical symptoms (1 attribute); DG = genetic

inheritance (4 attributes); OC = clinical observations

(7 attributes); OL = laboritorial observations (2

attributes). It is evident that nor all the data are

important for to the proposed analysis.

The selection of the important attributes to the

problem is a difficult task (Jones, 1998). Therefore,

each attribute should be analyzed in an individual

way and together. For example, the attributes (CC)

correspond to judgements of subjective character

and therefore they were eliminated of the group of

attributes. The original database needs to pass for a

selection process, enrichment, enhance, cleaning and

transformation, before they can be submitted to the

algorithms of data mining. In the next section are

described the data preparation stage and the data

mining techniques as discriminant rules.

3 APPLICATION OF THE KDD

PROCESS

The KDD methodologies vary from author to author

but all deal with the same necessary stages for

accomplishment of the process. In general lines the

stages of KDD involves: Selection, Pre-processing,

Data Mining, Interpretation and Visualization. Each

mentioned stage involves other steps that depend on

the specific problem. In the continuation will be

described the stages applied to the nephrolithiasis

database. The Figure 1 shows the structure of the

applied stages in this work.

3.1 PRE-PROCESSING DATA SETS

3.1.1 Removing Attribute

The criteria adopted for removal of attributes were

based on the database analysis and on the problem in

subject:

a) The first criterion determines as irrelevant the

attributes that had presented less than 3

DATA MINING APPLICATION TO OBTAIN PROFILES OF PATIENTS WITH NEPHROLITHIASIS

105

occurrences in the total of 363 records. 18 were

the total of excluded attributes.

b) The second criterion was the irrelevance of

attributes that do not contribute in the space

problem definition. Some of these were: related

attributes to the medical conduct and the field of

names. The field <medical conduct> is related

with the decisions taken for the doctors in

intention to treat the disease, and therefore it

does not possess direct relation with the

determination of its cause, and the field <name>

does not have any relation with the intended

analysis. 12 were the total of excluded

attributes.

3.1.2 Deleting Records

The criteria adopted for exclusion of records of the

database had been:

a) Elimination of the records of patients who had

presented empty fields of symptoms, due to the

main objective, that is discovery the disease

symptoms patterns.

b) Elimination of inconsistent records. As

example, the weight of a child of 300 Kg can be

cited (certainly typing error) for which was

opted to not correcting (established in similar

records) in way to not polarize the discovered

knowledge. 12 were the total of excluded

records.

3.1.3 Enhancement

From the literal attribute <current clinical situation>

contained in the category {OC}, had been created 5

new attributes. They were: Abdominal pain,

Hematuria, Renal Colic, Asymptomatic, ITU, These

fields had been created due to high frequency of

occurrences.

3.1.4 Transformation of Attributes

The following transformations had been applied to

some attributes of the database:

a) For the <date> fields that possessed the format:

"dd/mm/yy", had been transformed for the age

into years "yy". This transformation was

realized in the following fields: <DateOfBirth>,

<DateOfDiseaseBeggining> and

<DateOfDiseaseBeginningOfControl>.

b) Fields with the measure in meters <height>, had

been transformed into centimeters.

c) Attributes referring to the genetic inheritance

from the father, the mother or other familiar had

been substituted by an only attribute

<FamiliarHereditarySucession>.

d) Attributes referring to the symptoms Hematuria

(X), Macroscopic Hematuria (Y) (physical

result of the symptom Hematuria) and

Microscopic Hematuria (Z) (resulted of

laboratory exams of the symptom Hematuria)

had been grouped in an only attribute Hematuria

(W) obeying the following rule:

∨∨=

3.1.5 Removing Outliers

After realized the data elimination and the data

improvement, 351 were the total of valid records in

the database. In way to evaluate the dispersion of the

referring data to the patients’ symptoms (records),

the curve of Gauss was used to eliminate records

that presented accented deviation in relation to the

others. The adopted procedure is described as

follow:

The number of realized comparisons was 61425 for

the 351 records. For each combination of 2 records,

the numbers of occurrences of equal symptoms for

the involved patients had been counted. The

expressions (2) and (3) show the adopted strategy.

M ,...,1for }1,0{s where

},...,,,{

321

ssssS

iiii

(2)

contSnotXORScont

lkMlkC

kll

,...,1 where

)(

with ,...,1,for

<=∀

(3)

where: M is the number of patients, N is the number

of symptoms,

S is the vector of symptoms of the

“i” patient,

s is the “j” symptom of the “i”

patient and

C is the combination of the “k”

patient with “l” patient. In this work, M=351 and

N=14.

For elimination of a register the follow criteria was

adopted: Records of patients with less number of

occurrences than

)(2 xSX − (where

is the

average of occurrences

cont and )(xS is the

standard deviation of the sample) can be

inconsiderate. We verified that none records were

removed with this consideration due to the little data

dispersion.

ICEIS 2004 - ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT SYSTEMS

106

3.2 DATA MINING

In this section the objective is to obtain discriminant

rules that characterize the symptoms patterns.

Through these patterns is possible to discovery

patients’ profiles apt to acquire the disease. In the

continuation the followed procedures will be

described.

3.2.1 Obtaining Characteristic Patterns of

the Disease

In this stage the objective is to identify the disease

characteristic patterns. For that the data were

separated in patient and symptoms keeping its

relationship (1:1). See Figure 2.

Figure 2: Databases structures

Through an analysis of the symptoms database (that

contain patient's sick and not sick data), 150 distinct

patterns had been observed, approximately,

involving all the symptoms. In way to reduce the

number of patterns, the occurrences percentage of

the presence

PercS and the absence PercNS

of each symptom were calculated. The expression

for calculation is given by the expression (4) and the

values are shown in Table 1.

Be the vector of symptoms of the patient “i”:

},...,,,{

321

iiii

ssssS = given by the equation (2)

NjforMs;PercS sfor

,...,1 /)(1

===

∑

(4)

PercSPercNSsfor −== 0.1 ;0

(5)

In Table 1, “Sick” represents the relative frequency

of sick people who present the symptom "j" and

“NS-Sick” is the relative frequency of sick people

who had not presented the symptom "j". Through

this Table is possible to obtain conclusions of the

type:

...1 %])[_( %])[0(

(6) %])[_( %])[1(

NjtSickNSwithqs

tSickSwithqs

jjj

=∀=∀

⇔=∀

Table 1: Occurrences Percentage

Symptom

PercS

S-Sick NS-Sick

0.63 0.70 0.96

0.04 1.00 0.79

0.31 0.80 0.80

0.01 0.80 0.80

0.42 0.80 0.79

0.15 0.67 0.82

0.07 1.00 0.78

0.12 1.00 0.77

0.49 0.74 0.85

0.20 0.87 0.78

0.08 0.96 0.78

0.05 1.00 0.79

0.02 1.00 0.79

0.49 0.68 0.91

Using the expression (6) is possible to obtain the

following rules:

%]96)[_( %]37)[0(

%]70)[_( %]63)[1(

SickNSwiths

SickSwiths

⇔=

%]79)[_( %]58)[0(

%]80)[_( %]42)[1(

SickNSwiths

SickSwiths

⇔=

Notice in the Table 1, that there are small values for

PercS (for example:

s ,

s ). In way to reduce

the number of different patterns, a criterion was

applied based on a heuristic rule through which the

less characteristic symptoms of the disease are

retired. The rules adopted were:

SsThenfreqSick

ANDPercSif

∉<

≤<

)

10.0(0.03

(7)

SsThenPercSif

∉≤ )03.0(

(8)

where

freqSick represents the frequency of sick

people for the symptom

s .

is a adjustment

parameter that determines how much a symptom can

be considered or not. In this work it was chosen as

85.0=

. With the heuristic rule proposed, the

number of symptoms reduced to 12. To obtain

patterns classification, the characteristic weight was

calculated for each vector

S through the equation:

(9):

Original

Symptom

Patients

1:1

DATA MINING APPLICATION TO OBTAIN PROFILES OF PATIENTS WITH NEPHROLITHIASIS

107

sparaPercNS

sparaPercS

Weigth

∑

⎪

⎭

⎪

⎬

⎫

⎪

⎩

⎪

⎨

⎧

(9)

The table 2 shows the six (6) first ones resultants

patterns of that classification that presented larger

occurrence.

Table 2: Patterns classification for the proposed method

Notice that the patterns classification doesn't

determine the most characteristic pattern of the

disease. That classification only informs the most

frequent patterns considering the presence or

absence of a symptom. That strategy allows

diagnosing symptoms patterns that match or not with

the disease.

Obtaining Discriminant Rules

Starting from the classification of Table 2,

discriminant rules of the expressed type were built in

(10). The rules allow determining how much a

pattern characterizes patients sick and not sick.

Considering

where},...,,{

21 r

pkpkpkPk =

...1 symptomsr =

as being the group of patterns

found in the database, the discriminant rules are

given by:

]2)[(]^1)[(

,..,1 ),(

wSfreqSickwSreqPac

NjpkswhereS

⇒=∀=∀

(10)

The rule should be read as: for any patient "i" that

possesses the symptoms pattern P has the

probability of occurrence of w1 and the probability

of being sick of w2.

Applying the patterns of the Table 2 to the

symptoms database and considering the expression

(10) was obtained the following discriminant rules:

%]91,40)[(%]^27.6)[(

1 ),(

SfreqSickSfreqPac

PSwhereS ⇒=∀

%]29.64)[(%]^00.4)[(

4 ),(

SfreqSickSfreqPac

PSwhereS ⇒=∀

Obtaining the patients’ profiles

In way to identify patients’ groups that characterize

the disease, were applied the discriminant rules in

the patients’ database. The Table 3 shows data of

patients for each rule.

4 RESULTS

In the Table 1, was observed that 63% of the patients

presented the symptom

s and 70% of them had

nephrolithiasis. On the other hand, 37% of the

patients didn't present the symptom

s , and 96% of

them are sick. In the same way, 42% of the patients

presented the symptom

s , and 80% of them are

also sick patients. On the other hand 58% of the

patients didn't present the symptom

s , and 80% of

them were sick patients.

The previous results do not allow us extract

conclusions if the symptom characterizes or not the

disease. For that reason was necessary a

classification of patterns considering the presence

and the absence of symptoms. The Table 2, shows

the most important patterns, where can be observed

that the symptoms

s ,

s e

s have a

certain relevance. The symptom

s corresponds to

HiperCalcidio,

s corresponds to HiperUricidio,

corresponds to HipoCitratidio,

s corresponds to

HFLRFamily (genetic inheritance) and

corresponds to Hematuria.

Through the Table 2, is observed that the pattern P4

almost have all the characteristic symptoms for the

disease. Its representation was expressed through the

rule:

%]29.64)[(%]^00.4)[(

4 ),(

SfreqSickSfreqPac

PSwhereS ⇒=∀

Pattern Weight Occurr

P1={1,0,0,0,0,0,0,1,0,0,0,1} 0.76448 22 6.27

P2={1,0,0,1,0,0,0,0,0,0,0,0} 0.75427 10 2.85

P3={1,0,0,1,0,0,0,0,0,0,0,1} 0.75309 10 2.85

P4={1,0,0,1,0,0,0,1,0,0,0,1} 0.75095 14 4.00

P5={1,0,1,0,0,0,0,1,0,0,0,1} 0.73243 11 3.14

P6={0,0,0,0,0,0,1,0,0,0,0,0} 0.68329 16 4.57

ICEIS 2004 - ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT SYSTEMS

108

Table 3. Patients’ characteristics for each pattern

which is possible to conclude: from the 351

analyzed patients, 4% (14 patients) presented the

pattern P4 and 64.29% (9 patients) of them were

sick. For the pattern P1, 6.27% (22 patients) of the

patients had this pattern, and 40.91% (9 patients)

were sick.

%]91,40)[(%]^27.6)[(

1 ),(

SfreqSickSfreqPac

PSwhereS ⇒=∀

Notice in Table 3, that the fields <Current Age>,

<Start-Disease>, <Weight> and <Height> are not

important in relationship to <sex> and <Predominant

Race>. For the P1 rule is possible to say that patient

of masculine sex and of white race is able to acquire

the disease. This last conclusion is very superficial

due to data sets that referring to the feeding they are

not available. For future works will be considered

nutritious habits and areas where the patients live.

5 CONCLUSIONS

In this work, an application of the KDD

methodology for knowledge discovery in the

database for nephrolithiasis was applied. The

problem of the medical diagnosis for this type of

disease, especially in children, is a difficult work of

diagnose and advancing. For that reason the

database of the sick and not sick was considered.

That influence in a positive way in the medical

sector and it affects positively in the medical

diagnosis.

The work was divided in three stages: the first stage

corresponds to the identification of patterns in

clinical and laboratorial symptoms, to characterize

the disease in sick and not sick patients. For the

second stage discrimination rules were used on the

patterns of discovered symptoms. The last stage

corresponds to a discrimination process, of the

patient's profile, based on the obtained rules.

The limitation of the number of data sets is a great

obstacle for the success of Data Mining algorithms

and for the KDD methodology. It is possible to

discover patterns (knowledge) valid for a group

specific of data sets. The current researches should

give attention to that subject. The database used in

this work contains 351 records of patients with

clinical and laboratorial symptoms. In spite of the

“little” amount of data, is probably the only database

in the net of hospitals in the Belo Horizonte city in

Brazil.

It is necessary methodologies that allow extract

knowledge in database with few records. In this

paper are proposed some heuristic techniques

capable to work with few data. If the KDD

methodology and Data Mining try to discover gold,

perhaps be in the hour of we begin to seek that gold

under our feet, in smaller and more accessible

database than the great databases. Another great

challenge can be in front of us.

REFERENCES

Fayyad, U.M., G. Piatetsky-Shapiro, P. Smyth, and R.

Uthurusamy, editors. 1996. Advances in Knowledge

discovery and data mining. Menlo Park, CA: AAAI

Press/MIT Press.

Pyle, D. 1999. Data Preparation for Data Mining. Morgan

Kaufmann Publishers, Inc. San Francisco, California.

Bojarczuk, C; Lopes, H. Freitas, A. Data Mining with

Constrained Syntax Genetic Programming:

Applications in Medical Data Set. Intelligent Data

Analysis in Medicine and Pharmacology. IDAMAP01,

London, UK, September, 4

, 2001.

Werner, J.; Fogarty, T. Genetic Programming Applied to

Severe Diseases Diagnosis. Intelligent Data Analysis

in Medicine and Pharmacology. IDAMAP01, London,

UK, September, 4

, 2001.

Jones, M.D. 14 Powerful Techniques for Problem Solving.

Times Books Random House Inc. 1998

Pattern Sex (%) Race (%) Current age

M F 1 2 3 4 5

)(xS

Start-Disease

Weight (kg)

Height (cm)

1 62 38 44 6 37 0 13 18.5 1.2 0 – 9 26.60 130

2 63 37 55 9 36 0 0 13.5 5.8 0 – 10 24.81 126

3 45 55 82 4 10 4 0 13.1 4.0 2 – 10 22.22 122

4 40 60 30 10 50 0 10 15.4 6.4 0 – 6 29.00 112

5 80 20 50 0 40 10 0 14.4 4.9 0 – 10 19.30 114

6 46 54 55 15 15 0 15 11.6 3.4 2 – 10 23.84 116

DATA MINING APPLICATION TO OBTAIN PROFILES OF PATIENTS WITH NEPHROLITHIASIS

109