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Expert System with Application
journal homepage: www.elsevier.co m/locate/eswa
Thyroid Diseases Diagnosis Expert System Using Decision Tree
Vivi and Derwin Suhartono
Computer Science Department, Bina Nusantara University, Jakarta, Indonesia
ARTICLE INFO
ABSTRACT
Keywords:
Expert system
Thyroid
Disease
Diagnosis
Decision Tree
Thyroid gland is an important organ of the endocrine system. It releases hormones
which control body metabolism. However, around two hundreds million
populations worldwide had suffered from thyroid disease. Clinicians often
experience difficulties in the diagnosis of thyroid diseases due to the inherent
complexity of the clinical process and lack of comprehensive automated
diagnostic tools. Thus, a decision support system based on expert system has been
projected according clinical symptoms as predictive variable in decision tree. The
aim of this study is to help improve the performance of thyroid clinical
assessment, specifically from the clinical decision support perspective. A
retrospective data analysis of the clinical studies evaluating sixteen symptom risk
factors implemented for the development of diagnosis models to distinguish six
kinds of thyroid related diseases. The results of the thyroid diseases diagnosis
expert system evaluation had reached 86, 67% accuracy which matched with
those of the physicians’ decisions.
1. INTRODUCTION
Thyroid hormones affect various metabolic events. As a
the changes of these hormones is required a hormones test
which can be costly in the developing country.
result, occurrence of function changes in the various
The increasing need to develop a more basic and
organs is resulted if thyroid disease begins. The symptoms
accessible approach need to be taken in hand in order to
vary from palpitation, dyspnea, exophthalmoses and
cut down the cost as well as the time has drawn the
dysphagia. Its pathogenesis involves both genetic and
attention of a diverse array of fields, including artificial
environmental factors. The long-term persistence of
intelligence and biomedical engineering, explaining why
thyroid diseases can cause susceptibility to specific
related technologies such as expert systems and decision
complications depending on the degree of thyroid disorder.
tree have been adopted for clinical decision support
Although there is a risk in every age and gender, thyroid
system.
disease is often observed as the person gets older and
For instance, Chang et al. (2012) developed a hybrid
women are 7 times more prone to thyroid gland disease
decision support model to discover informative knowledge
(National Women’s Health Information Centre, 2013).
in diagnosing acute appendicitis. This study had develop a
Thyroid gland insufficiency is easily defined with the
simple and reliable hybrid decision support model by
measurement of free T3 (FT3), free T4 (FT4), TSH, TT3
combining statistical analysis and decision tree algorithms
and TT4 hormones before the treatments begins. However,
to ensure high accuracy of early diagnosis in patients with
suspected acute appendicitis and to identify useful decision
Sindh University Research Journal published in 2011
rules. Statistical analysis approaches were used as a feature
featured a knowledge based expert system for symptomatic
selection process in the design of decision support models,
automated healthcare which developed by Soomro et al. It
including the Chi-square test, Fisher’s exact test, the
is a web application which can be a good solution for the
Mann-Whitney U-test (p < 0.01), and Wald forward
medical field, where the doctors and patient are too far
logistic regression (entry and removal criteria of 0.01 and
with each other, but the system which interact to patient be
0.05, or 0.05 and 0.10, respectively).
a computer and performs the diagnosing of disease which
The final decision support models were constructed
is disease recognition by selecting symptoms, and then
using the C5.0 decision tree algorithm of Clementine 12.0
symptoms are checked from the knowledge base for the
after preprocessing. A statistically significant difference
recognition of the disease and suggests treatment of
was detected in the pair wise comparison of ROC curves (p
disease.
< 0.01, 95% CI, 3.13-14.5; p < 0.05, 95% CI, 1.54-13.1).
It also includes the information portion disease
The larger induced decision model was more effective for
information containing list of diseases where user can view
identifying acute appendicitis in patients with acute
the disease, it also contains the laboratory test portion,
abdominal pain, whereas the smaller induced decision tree
which physically doesn’t test, but by inserting the resulted
was less accurate with the test data.
values to a particular test, it will check the parameter
Takada et al. (2012) developed a prediction of axillary
lymph node metastasis in primary breast cancer patients
values of report and make a process to identify disease and
tell a solution for the disease from knowledge base.
using a decision tree-based model. This study aim to
Motivated by the worldwide increasing number of
develop a new data-mining model to predict axillary lymph
research in clinical decision support system each year,
node (AxLN) metastasis in primary breast cancer by
researchers aim to help health care professionals in
decision tree-based prediction method—the alternating
diagnosis of thyroid disease. Using the basic characteristic
decision tree (ADTree). Clinical datasets for primary breast
symptom of thyroid related diseases will be much more
cancer patients who underwent sentinel lymph node biopsy
efficient. These characteristic symptom form great data
or AxLN dissection without prior treatment were collected
stacks when used in each person’s diagnosis. Therefore, a
from three institutes (institute A, n = 148; institute B, n =
decision support system which will be helpful for the
143; institute C, n = 174) and were used for variable
diagnosis of thyroid related disease
selection,
model
training
and
external
validation,
respectively.
2. LITERATURE REVIEW
The models were evaluated using area under the
In this part, the information related with the easier
receiver operating characteristics (ROC) curve analysis to
conception of the decision support system’s construction in
discriminate node-positive patients from node-negative
thyroid disease diagnosis is introduced in the shape of sub-
patients. The ADTree model selected 15 of 24 clinic
classifications.
pathological variables in the variable selection dataset. The
2.1 THYROID
resulting area under the ROC curve values were 0.770
Thyroid gland is one of the important endocrine
[95% confidence interval (CI), 0.689–0.850] for the model
organs in human body. It is located at the front of the
training dataset and 0.772 (95% CI: 0.689–0.856) for the
neck under Adam’s apple where collarbones meet. The
validation dataset, demonstrating high accuracy and
gland produces hormones like triiodothyronine (T3)
generalization ability of the model. The bootstrap value of
and thyroxine (T4) that control the way every cell in the
the validation dataset was 0.768 (95% CI: 0.763–0.774).
body uses energy or metabolism (National Institutes of
Health, 2013). Controlling metabolism is essential for
Expert system is defined as one, which contains
regulating weight, body temperature, mental and
information obtained from a human expert, and
physical energy levels. As time goes by, thyroid gland
represents that information in the form of rules, such as
might degrade and encounters some age-related
IF–THEN. The rule can then be used to perform
problem.
operations on data to inference in order to reach
When the body releases too much thyroid hormone,
appropriate conclusion. These inferences are essentially
the condition is called hyperthyroidism. And an
a computer program that provides a methodology for
underactive thyroid gland leads to hypothyroidism.
reasoning about information in the rule base or
Several thyroids related medical conditions are De
knowledge base, and for formulating conclusions
Quervain’s thyroiditis, Hashimoto thyroiditis, Grave’s
(Hemmer, 2008).
disease and Plummer’s disease. These thyroids related
medical condition occasionally quite similar in some
basic symptoms fortunately it is still distinguishable by
few dominant characteristic symptoms.
Figure 2. The Structure of Expert System
2.3 DECISION TREE
A decision tree takes as input an object or situation
described by a set of properties, and outputs a yes or no
Figure 1. The Anatomy of Thyroid Gland
decision. Decision trees therefore represent Boolean
functions. Functions with a larger range of outputs can
2.2 EXPERT SYSTEM
Expert system is a branch of applied artificial
intelligence, an area of computer science focusing on
creating machines that can engage on behaviors that
humans consider intelligent and was developed by the
artificial intelligence community in the mid-1960s.
The basic idea behind expert system is simply that
expertise, which is the vast body of task-specific
knowledge, is transferred from a human to a computer.
This knowledge is then stored in the computer and
users call upon the computer for specific advice as
needed. The computer can make inferences and arrive
at a specific conclusion. Then like a human consultant,
it gives advices and explains, if necessary, the logic
behind the advice (Russell & Norvig, 2010).
also be represented, but for simplicity we will usually
stick to the Boolean case. Each internal node in the tree
corresponds to a test of the value of one of the
properties, and the branches from the node are labeled
with the possible values of the test. Each leaf node in
the tree specifies the Boolean value to be returned if
that leaf is reached (Kendall & Creen, 2007).
After the each node in decision trees are filled by a
statement, 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 inference mechanism.
These rules can be checked in terms of application
by showing them to an expert whether they are
meaningful or not. The decision trees are used in the
analysis and applications of classifying various cases
into low-mid or high risk groups; creating rules in order
to predict the future events; definition of the relations of
the certain sub-groups; the unit of the categories and
getting the most effective decisions by the help of the
medical observation (Poole & MackWorth, 2010).
3. METHODOLOGY
Developing an expert system involves tasks such as
acquiring knowledge from an acknowledged domain
expert, documenting it and organizing it, generating
knowledge net to check the relationships between different
knowledge sources, checking for consistency in the
Yes
No
knowledge and finally transforming the knowledge net into
Figure 3. Decision Tree.
a computer program using appropriate tools.
The expert system operation is achieved via the decision
trees structure which is related as one of disease diagnosis
techniques. The basic characteristic symptom of thyroid
related diseases will be inquire and assess with the rule
stored in knowledge base. This expert system uses the
knowledge of the domain which acquired from thyroid
expert to ensure its credibility and reliability.
Below are the symbol descriptions corresponding with
those shown in decision tree. Table 1 describes the
symptoms of thyroid diseases which represented in
decision tree node by Sn symbol. Table 2 explains about
thyroid diseases where in decision tree node is represented
by Dn symbol.
Later this knowledge included disease and symptom
Table 1. Symptoms of Thyroid Diseases.
related toward condition was coded in knowledge base and
through inference engine, a specified control strategy or
search techniques to arrive at solutions which search
through the knowledge base to arrive at decisions.
Symbol
S01
Weight loss
S02
Sleeping difficulty (insomnia)
Basically, knowledge base is the state space and the
inference mechanism is a search process.
Joint and muscle pain OR limb
S03
The expert system diagnoses thyroid disease by posing
series of questions about the characteristic symptom which
S05
S06
identified by following the path from the topmost node, the
root, to a node without children, a leaf, according to the
answers that most reflect the symptom.
sensitive toward light OR protruding
eyes (exophthalmoses)
thereby form a hierarchy, encoded as a decision tree. In the
node has a ‘yes’ child and a ‘no’ child. A disease is
jaw area OR ear area (tender goiter)
Swelling around the eyes OR
contained in a node, and every node points to child node
simplest form is ask yes-or-no questions, and each internal
numbness
Tenderness around neck area OR
S04
will be used as predictive variable. Each question is
for each possible answer to its question. The questions
Symptom
S07
Inflammation around neck area OR
jaw area OR ear area (swelling
goiter)
Increased appetite OR frequent
bowel movement (diarrhea) OR
hand tremor
Table 2. Thyroid related Diseases.
Symbol
Disease
D1
De Quervain’s Thyroiditis
D2
Hyperthyroid
D3
Grave’s Disease
D4
Plummer’s Disease
D5
Hashimoto Thyroiditis
D6
Hypothyroid
D7
Undefined disease
Figure 5. Disease List.
The established expert system was further evaluated its
prediction accuracy. For the ensemble procedure, the data
IF-THEN rules are simple but powerful forms of
knowledge representation providing the flexibility of
combining declarative and procedural representation for
using them in a unified form. IF-THEN rule has a set of
were randomly sampled patients with variety in gender and
diseases. Of 30 patients as random samples evaluated in
this study, 26 patients can be detected and identified the
kind of thyroid disease by expert system. The detected
antecedents and a set of consequents. The antecedents
thyroid related diseases are De Quervain’s thyroiditis,
specify a set of symptoms and the consequents a set of
hyperthyroid,
diseases. For instance Grave’s disease is detected through
the IF-THEN rule below.
IF (weight loss = yes) AND (sleeping difficulty
/insomnia = no) AND (swelling around the eyes OR
sensitive toward light OR protruding eyes/exophthalmoses
= yes) THEN Grave’s disease.
4. RESULT
Grave’s
disease,
Plummer’s
disease,
Hashimoto thyroiditis and hypothyroid.
Due to some of the characteristic symptoms especially
eyes irritations that found in Grave’s disease appear to be
more dominant in women than men. As a result, there are 4
undetected patient samples who suffer Grave’s diseases are
all happen to be men who doesn’t experience swelling
around the eyes, neither sensitive toward light or
Following are the screenshot of the established thyroid
diseases diagnosis expert system using decision tree.
protruding eyes (exophthalmoses).
From the above experiments, prediction accuracy of
thyroid disease diagnosis expert system can be calculated
from the ratio of total sample that can be detected by the
expert system with the total sample which is randomly
taken.
By using existing patient data which had been validate
by physician decision to estimate the diagnostic accuracy
of thyroid disease diagnosis expert system. The result
showed decision tree was effective in detecting thyroid
related
Figure 4. Main Menu.
diseases.
The
diagnostic
accuracy
was
approximately 86, 67% from 30 random sample patients.
total sample detected by the expert system
the total sample which is randomly taken
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Automated Healthcare. Sindh University Research
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