Doğu Anadolu Bölgesi Araştırmaları; 2007
Şengül DOĞAN, İbrahim TÜRKOĞLU
HYPOTHYROIDI AND HYPERTHYROIDI DETECTION
FROM THYROID HORMONE PARAMETERS BY USING
DECISION TREES
*Şengül DOĞAN, **İbrahim TÜRKOĞLU
*Firat University, Department of Bioengineering, 23119 Elazig / TURKIYE
**Firat University, Department of Electronics and Computer Science, 23119 Elazig / TURKIYE
vsenguldogan@hotmail.com, iturkoglu@firat.edu.tr
__________________________________________________________________________________________________________________________________________________
ABSTRACT
In this study a decision support system has been projected from the biochemistry blood parameters which
will be very helpful and will make everything easier for the physicians in the diagnosis of Hyperthyroidi and
Hypothyroidi. Based on pattern recognition process, the system operation is achieved via the decision trees
structure which is related as one of the data mining techniques. The basic characteristic of the thyroid
hormone parameters that is, TSH, FT3, FT4, TT3 and TT4 parameters are used in the process of entering the
system and finally Hyper(+), Hypo(+) and (-) results have been evaluated at the end of this process. Data of
120 patients are evaluated in the projected system. The results of the decision support system have
completely matched with those of the physicians’ decisions.
Keywords: Pattern Recognition, Data Mining, Decision Trees, Tyhroid Hormones, Expert System.
__________________________________________________________________________________________________________________________________________________
KARAR AĞACI YÖNTEMİNİ KULLANARAK TİROİD
HORMON PARAMETRELERİNDEN HİPERTİROİDİ VE
HİPOTİROİDİ TEŞHİSİ
ÖZET
Bu çalışmada, biyokimya test sonuçlarından Hipertiroidi ve Hipotiroidi teşhisinde, hekime yardımcı olacak
ve kolaylık sağlayacak bir karar destek sistemi tasarlanmıştır. Örüntü tanıma süreci esas alınmış olup, sistemin işleyişi veri madenciliği tekniklerinden olan karar ağaçları yapısı ile sağlanmaktadır. Sisteme giriş
olarak, tiroid hormonlarından temel belirleyiciler olan TSH, FT3, FT4, TT3, TT4 parametreleri kullanılarak,
çıkış olarak ta Hiper(+), Hipo(+) ve (-) değerlendirmelerin de bulunulmuştur. Tasarlanan sistemde 120 hasta
verisi değerlendirilmiştir. Karar destek sisteminin sonuçları, doktorun verdiği kararlarla tamamen örtüşmüştür.
Anahtar Kelimeler: Örüntü Tanıma, Veri Madenciliği, Karar Ağacı, Tiroid Hormonları, Uzman Sistemler
__________________________________________________________________________________________________________________________________________________
1. INTRODUCTION
Thyroid hormones affect various metabolic
events. As a result, there occurs changes in the
functions of various organs in the organism if tyroid
disease begins. Bradicardi diminishing in pulse
pressure, decrease in the heart flow and decrease in
the left verticule contramass are the findings of
Thyroid Hormone Deficiency[1]. This very state is
defined as Hypothyroidi a disease which often
occurs because of otoimmun reasons. Although
there is a risk in evey age, hypothyroidi is often
observed as the person gets older and after the age
of 60 hypotyhroidi is observed with the percentage
of %2-4 [2]. Thyroid Gland Insufficiency (TSH) is
easily defined with the measurement of free T3
(FT3) and free T4 (FT4) hormones and then the
treatments begins [3]. Tachycardia, increase in the
pulse pressure, in heart flow and in left ventrikül
contramass are observed during the Thyroid
Hormones Abundancy [1] and this state is defined
as hyperthyroidi. It is enough fort he diagnosis if
there are some situations reminding of hyperthyroidi
with various symptoms or a decrease in TSH
observation mad efor detection and also if there is a
high level of T4 or T3 [4]. At present, both total
Tiroxin (TT3) and total Triiodothyronine and also
free tiroxin and free Triiodothyronine serum enzyme
levels can directly be measured in the serum. As a
rule, changes of serum enzyme levels are taken as
the base in the diagnosis of hyperthyroidi hypothyroidi [5].
TSH, FT3, FT4, TT3 and TT4 are the
enzymes taken into consideration in the diagnosis of
Hypertyroidi-Hypotyroidi [6-9].
These enzymes form great data stacks when
used in each person’s diagnosis. Also, as these kinds
of researches in the biology and medical science
world increase, this will cause the increase in
different kinds of data related with the real-life cases
[10]. It became quite difficult for people to be able
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to comprehend and change hundreds of
characteristics and thousands of images into
meaningful knowledge in modern medical science
[11, 12] DNA [13, 14] and protein synthesis [15],
biological state measurements [16], graphs [17] and
enzymes [10] are some of the main types of data
which are still being used. As the data increased it
became quite a boring and difficult work for the
medical science experts to prepare a guide that will
be used in the processes of reading, simplifying,
classifying the findings and making a decision at the
end. Also a lot of findings hidden in these data have
remained as the data stack in this way. It is
necessary to get these data automized in order to
obtain useful information. Getting information from
the databases or the data mining is the kind of
method mostly used for solving these kinds of
problems [18]. Data mining is used for both getting
the information related with the text and the
information of the image data of an individual
patient and then for matching each patient’s entry
[19]. In this context data mining has been applied so
successsfully in many fields of medical science [1820]. For instance, there are a lot of work that can be
seen in the literature related with discovering certain
rules in the diagnosis and treatment of acute
ailments; applying the determined rules and making
complementary data mining for defining the
enlargement states of the working procedures of the
refined, shared, organized and produced information
in the system [21] and also there are some works
related with projecting a Data Acquiring System for
the baby deaths due to the Hypoplastic Left Heart
Syndrome and making quick and true guesses using
the acquired data [22]. Data warehouses in medicine
field are called clinical warehouse too. Then, those
clinical stores containing biological, clinical and
administrating data unit the patients information.
[23].
of patterns to features that are regarded as a
condensed representation, ideally containing all the
necessary information. In the next stage, the feature
selection step, a smaller number of meaningful
features that best represent the given pattern without
redundancy are identified. Finally, classification is
carried out: a specific pattern is assigned to a
specific class according to its characteristic features
selected for it. This general abstract model, which is
demonstrated in Figure 1, allows a broad variety of
different
realizations
and
implementations.
Applying this terminology to the medical diagnostic
process, the patterns can be identified, for example,
as particular, formalized symptoms, recorded
signals, or a set of images of a patient. The classes
obtained represent the variety of different possible
diagnoses or diagnostic statements [24]. The
techniques applied to pattern recognition use
artificial intelligence approaches [25].
patterns
feature
extraction / selection
classes
classification
Figure 1. Pattern Recognition System.
2.2. THYROID HORMONE PARAMETERS
Thyroid-Stimulating Hormone (TSH):The thyroid
is unique among the endocrine glands because it has
a large store of hormone and a slow rate of normal
turnover. Stimulation of the thyroid glan by the
TSH, which is produced by the anterior pituitary
gland, causes the release and distribution of stored
thyroid hormones. TSH stimulates secretion of T 4
and T3. TSH secretion is physiologically regulated
by T3 and T4 (feedback inhibition) and is stimulated
by thyrotropin-releasing hormone (TRH) from the
hypothalamus.
Free Triiodothyronine (FT3): This is one of the
determinations used to evaluate thyroid function and
measure that fraction of the circulatory T 3 that exists
in the free state in the blood, unbound to protein.
FT3 is done to rule out T3 toxicosis, to evaluate
thyroid replacement therapy, and to clarify proteinbinding abnormalities.
Free Thyroxine T4 (FT4): Free thyroxine (FT4)
comprises a small fraction of total thyroxine. The
free FT4 is unbound to protein and available to the
tissues, and it is the metabolically active form of this
hormone. This fraction constitutes ~5% of the
circulatory thyroxine T4 [26].
In this study, a decision support system
which will be helpful for the diagnosis of
hyperthyroidi and hypothyroidi is improved to be
used for the aim of classifying the decision tree
construction in the data mining techniques. Thyroid
hormone parameters in biochemistry data were used
for application and 120 patients’ data have been
evaluated successfully.
2. THEORETICAL VIEW
In this part, the information related with the
easier conception of the decision support system’s
construction in hyperthyroidi and hypothyroidi
diagnosis is introduced in the shape of subclassifications.
2.3. DATA MINING
Data mining is the work of achieving the
information that enables us to find the relations
among the great numbers of data which will be very
helpful for making guesses about the future by using
a computer programme. That is, the aimed
information is obtained by processing the data. Data
2.1. PATTERN RECOGNITION
Pattern recognition can be divided into a
sequence of stages, starting with feature extraction
from the occurring patterns, which is the conversion
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mining is used in many fields such as biomedicine,
gene functions, data analysis of DNA arrangement
pattern, diagnosis of illnesses, retail data,
telecommunication industry, guesses about selling,
financial analysis and astronomy [27]. For instance,
due to the unsystematic data, the lack of control, the
usage of too many various kinds, being unable to
make a consistent and systematic analysis on
databases, comparing the examples and determining
the critical differences some of the standart
softwares used in diagnosis system were defective.
So, the physicians were in the opinion that these
softwares were short of many characteristics. In this
Original
Database
context, data mining which makes it possible to
make a basic analysis on the graphs related with
both the digital parameters and morphemes by the
help of an expert information system and which
stores the processes, administers the necessary
information and also which includes a very useful
construction such as a reference for decision, took
an active position. [28]. In fact data mining is
evaluated as a part of the information discovery
process both in medical science and in the other
fields [29]. Data mining stages are presented in
Figure 2 [30].
Selected
Database
Discovered
Patterns
Obtained
Results
Figure 2. Data Mining Processes.
Data mining has an interaction with the user
and the database. Interesting data patterns are
showed to the user. And also, they can be saved in
the database if wanted [31]. Firstly the necessary
data are taken, classified and then processed while
obtaining meaningful information from the
databases [12]. Classification in data mining is used
for an automatic definition of the interesting object
in great data and for the information discovery in
the applications including the classification of the
trend in the market [32]. There are a lot of methods
used for classifying these data. In data mining the
decision trees among these techniques, which have
the characteristics of making guesses and defining,
have the most common usage in the classification
models because of the following reasons: [33]
by the help of the medical observation [25,35]. Rule
Extraction Structure is given in table 1 [36].
Table 1. Rule Extraction.
If (Rule1) then (Decision1)
Else if (Rule2) then (Decision2).
Rule= <a1> and … and <an>
Decision= Ψ, ΨЄ[ Ω1….Ωm ]
Here each one of decision class’ parts that
will be taken in rules is represented with Ω and the
result that will be got from that class parts is
represented with Ψ. The preparation of such a
decision list constitutes an important step in a
medical application and a decision tree can be
created easily according to these rules[18]. A
decision tree algoritm that is created according to
these rules constitutes a decision tree in the context
of information obtainment [37].
 Inexpensiveness of the organization,
 Easiness in interpretation,
 Their possibility of being easily integrated with
the database systems,
 More reliability.
Rule 1
2.4. DECISION TREES
After the data are created in decision trees,
the rules can be written down on the tree from root
to the leaf (If-Then rules). Getting decision in this
way provides the confirmation of the result of the
work of data mining. These rules can be checked in
terms of application by showing them to an expert
whether they are meaningful or not [34]. The
decision trees are used in the analysis and
applications of classifying various cases into lowmid or high risk groups; creating rules in order to
predict the future events; definition of the relations
of the certain sub-groups; the unition of the
categories and getting the most effective decisions
No
Yes
Decision
1
Rule 2
Yes
Decision
2
No
Rule N
Figure 3. Decision Tree Structure.
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database. The data collected are being processed in
this stage. For this, the rules underlined in table 2
and determined according to the physician’s
knowledge and then the decision tree in figure 5 was
obtained in relation with this knowledge.
3. DEVELOPED METHOD
The most common and main tests that help
the physicians decide on a diagnosis are biochemistry tests which are mostly successful in diagnosing. Hyperthyroidi and Hypothyroidi diagnosis can
be obtained by controlling the thyroid hormone
parameters which were taken into consideration for
the hyperthyroidi and hypothyroidi diagnosis in
biochemestry test results. The construction of the
improved decision support system is demonstrated
in Figure 4 based upon the pattern recognition given
in part 2.1 [24]. The functions of system are
constituted of these following steps:
Patient
data
PREPROCESSING
Data
acquisition
and
Filtering
Data
Step – 1: Pre-processing
Collecting the data is the step in which the
ones those are proper for the goal are choosen. The
necessary data were obtained by collecting the
biochemistry test results of the patients applied for
the internal medicine and by analyzing the ones
related with the hematology parameters with the
direction of the physicians. The database was
created by choosing the test results of the
Hyperthyroidi and Hypothyroidi suspected-ill from
the data collected.
FEATURE
EXTRACTION
and
CLASSIFICATION
Data Mining
and
Decision Trees
Decision Space
EVALUATION
Hiper(+) or
Hipo(+) or
Hiper(-) or
Hipo(-)
Step – 2: Feature Extraction and Classification
Figure 4. The Algorithm of Decision Support
This is the most important step of decision
System.
tree based decision support system which was
developed for processing the data in the collected
Table 2. The Rule Base Of Proposed System.
Decision Tree Index
A
B
B
C
D
E
E
F
G
H
H
İ
J
K
K
L
M
N
N
O
P
Q
Q
R
S
T
T
U
V
W
W
X
Rules
if (TSH>= 5) and (FT3<4) and (FT4<9) and (TT3<1,3) and (TT4<66) then
else if (TSH >= 5) and (FT3<4) and (FT4<9) and (TT3<1,3) and (TT4>=66) then
else if (TSH >= 5) and (FT3<4) and (FT4<9) and (TT3>=1,3) and (TT4<66) then
else if (TSH >= 5) and (FT3<4) and (FT4<9) and (TT3>=1,3) and (TT4>=66) then
else if (TSH >= 5) and (FT3<4) and (FT4>=9) and (TT3 <1,3) and (TT4<66) then
else if (TSH >= 5) and (FT3<4) and (FT4>=9) and (TT3 >=1,3) and (TT4<66) then
else if (TSH >= 5) and (FT3<4) and (FT4>=9) and (TT3 <1,3) and (TT4>=66) then
else if (TSH >= 5) and (FT3<4) and (FT4>=9) and (TT3 >=1,3) and (TT4>=66) then
else if (TSH >= 5) and (FT3>=4) and (FT4<9) and (TT3 <1,3) and (TT4<66) then
else if (TSH >= 5) and (FT3>=4) and (FT4<9) and (TT3 >=1,3) and (TT4<66) then
else if (TSH >= 5) and (FT3>=4) and (FT4<9) and (TT3 <1,3) and (TT4>=66) then
else if (TSH >= 5) and (FT3>=4) and (FT4<9) and (TT3>=1,3) and (TT4>=66) then
else if (TSH >= 5) and (FT3>=4) and (FT4>=9) and (TT3<1,3) and (TT4<66) then
else if (TSH >= 5) and (FT3>=4) and (FT4>=9) and (TT3>=1,3) and (TT4<66) then
else if (TSH >= 5) and (FT3>=4) and (FT4>=9) and (TT3<1,3) and (TT4>=66) then
else if (TSH >= 5) and (FT3>=4) and (FT4>=9) and (TT3>=1,3) and (TT4>=66) then
else if (TSH < 5) and (FT3<4) and (FT4<9) and (TT3<1,3) and (TT4<66) then
else if (TSH < 5) and (FT3<4) and (FT4<9) and (TT3<1,3) and (TT4>=66) then
else if (TSH < 5) and (FT3<4) and (FT4<9) and (TT3>=1,3) and (TT4<66) then
else if (TSH < 5) and (FT3<4) and (FT4<9) and (TT3>=1,3) and (TT4>=66) then
else if (TSH < 5) and (FT3<4) and (FT4>=9) and (TT3<1,3) and (TT4<66) then
else if (TSH < 5) and (FT3<4) and (FT4>=9) and (TT3>=1,3) and (TT4<66) then
else if (TSH < 5) and (FT3<4) and (FT4>=9) and (TT3<1,3) and (TT4>=66) then
else if (TSH < 5) and (FT3<4) and (FT4>=9) and (TT3>=1,3) and (TT4>=66) then
else if (TSH < 5) and (FT3>=4) and (FT4<9) and (TT3<1,3) and (TT4<66) then
else if (TSH < 5) and (FT3>=4) and (FT4<9) and (TT3<1,3) and (TT4>=66) then
else if (TSH < 5) and (FT3>=4) and (FT4<9) and (TT3>=1,3) and (TT4<66) then
else if (TSH < 5) and (FT3>=4) and (FT4<9) and (TT3>=1,3) and (TT4>=66) then
else if (TSH < 5) and (FT3>=4) and (FT4>=9) and (TT3<1,3) and (TT4<66) then
else if (TSH < 5) and (FT3>=4) and (FT4>=9) and (TT3>=1,3) and (TT4<66) then
else if (TSH < 5) and (FT3>=4) and (FT4>=9) and (TT3<1,3) and (TT4>=66) then
else if (TSH < 5) and (FT3>=4) and (FT4>=9) and (TT3>=1,3) and (TT4>=66) then
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Among the parameters given in the
biochemistry test results the normal values of
thyroid hormone parameters are given in Table 3.
Table 4. Performance of the Decision Support
System
Table 3. The Normal Values of Enzymes.
Thyroid Hormones
TSH
FT3
FT4
TT3
TT4
Total number of samples
Correct classification
Incorrect classification
Normal values
0,25-5 mg/dl
4-8,3
mg/dl
9-20
mg/dl
1,3-3,1 nmol/L
66-181 nmol/L
Hyperthyroidi Hypothyroidi
+
+
35
25
35
25
35
25
35
25
---------
5. DISCUSSION AND CONCLUSION
The increase in the knowledge obtained
under the light of biomedical searches, the processes
of reading, simplifying, classifying these findings
and making a decision are being so complex day by
day. These processes are automized after the data
mining took a part in this field. It has considerably
helped the medical experts and made it easier to
prepare a guide. Also, a lot of findings hidden
between the data mining and the data stacks
obtained in these kinds of fields, has turned to be
useful information in this way.
The advised system is based on pattern
recognition on which so many clever diagnosis
systems are constituted. There, the decision trees,
which take an important part in data mining for the
feature extracting and classification stage in the
pattern recognition process, have been used. Thus, a
decision support system is projected based on
pattern recognition and the function of the system is
based upon the data mining methods.
The developed decision support system will
considerably be helpful for the expert physicians
and practitioners for the interpretation of the
illnesses. This system construction can also be used
in the diagnosis of every illnesses in which the
criteria having certain parameters can be controlled.
Acknowledgments - We want to thank to,
Department of Biochemistry, Firat Medical Center,
Elazig, TURKEY, Dr. Bilal USTUNDAG for
providing the Biochemistry tests to us.
( Figure 5. )
Step – 3: Evaluation
In this step the datum formed at the end of the
Feature Extracting and Classification Process is
presented after evaluation. The results reached to
a,b,c,d,e,f,g,h,i leaves of the decision tree structure,
(figure 5) projected for this purpose, are evaluated
with hypotyroidi (+) diagnosis and the results
reached to the m,n,o,p,q,r,s,t,u leaves of the tree are
evaluated directly as hypertyroidi (+).
4. RESULTS
The developed decision support system was
experienced in the biochemical blood results of 120
patients and Hypertyroidi was diagnosed in 35 of
120 patients and Hypotyroidi was diagnosed in 35
of 120 patients. The other 50 patients were defined
healthy in the system. It is observed that these
results are overlapped successfully with the expert
physicans decisions. The rate of this success clearly
shows the effectivity and the reliability of the
Projected Decision Support System by the help of
the perfectly overlapped results and the decisions of
the experts in the diagnosis of Hyperthyroidi and
Hypothyroidi.
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Şengül DOĞAN, İbrahim TÜRKOĞLU
Figure 5. Structure of Decision Tree.
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