Skin infections still remain common in many rural communities in developing countries, with serious economic and social consequences as well as health implications. Directly or indirectly, skin infections are responsible for much disability (and loss of economic potential), disfigurement, and distress due to symptoms such as itching or pain. In this research, we built a Skin Infection Expert System for detecting skin infections and displaying the result of detection process. We describe five symptoms as major symptoms which include blister, itch, scaly skin, fever, and pain in the rash. Dempster-Shafer theory to quantify the degree of belief as inference engine in expert system, our approach uses Dempster-Shafer theory to combine beliefs under conditions of uncertainty and ignorance, and allows quantitative measurement of the belief and plausibility in our identification result.

1. Skin Infection Detection using Dempster-Shafer Theory
Andino Maseleno, Md. Mahmud Hasan
Abstract— Skin infections still remain common in many rural communities in developing countries, with serious economic and
social consequences as well as health implications. Directly or indirectly, skin infections are responsible for much disability (and
loss of economic potential), disfigurement, and distress due to symptoms such as itching or pain. In this research, we built a Skin
Infection Expert System for detecting skin infections and displaying the result of detection process. We describe five symptoms as
major symptoms which include blister, itch, scaly skin, fever, and pain in the rash. Dempster-Shafer theory to quantify the degree
of belief as inference engine in expert system, our approach uses Dempster-Shafer theory to combine beliefs under conditions of
uncertainty and ignorance, and allows quantitative measurement of the belief and plausibility in our identification result.
At least three forms of uncertainty can be identified as
I. INTRODUCTION
playing a significant role in these types of systems. The
Skin is the outermost tissue of the body and the first of these possibilistic uncertainty appears in
largest organ in terms of both weight and surface area. situations where the value of a variable can only be
It has an area of approximately 16,000 cm2 for an adult narrowed down to a set of values one of which is the
and represents about 8% of the body weight. Skin has a actual value of the variable. The second kind of
very complex structure that consists of many uncertainty is related to situations in which there exists
components. Cells, fibers and other components make uncertainty as to satisfaction of a predicate by an
up several different layers that give skin a multi-layered element. This is manifested by concepts which have
structure. Veins, capillaries and nerves form vast imprecise or gray boundaries. A very powerful tool for
networks inside this structure [1]. What makes the skin handling this type of uncertainty which also handles the
unique among organs is its exposed position up against first type of uncertainty is the fuzzy set. The third type
the outside world. Other body organs can function only of uncertainty is related to situations in which the value
in a controlled, protected environment where the of a variable assumes can be modeled by the
temperature never varies far from 98.6 degrees performance of a random experiment [6]. Figure 1
Fahrenheit. The skin maintains this environment, and to shows the process for dealing with uncertainty in Skin
do so, it must be able to take on temperatures ranging Infection Expert System.
from dry desert heat to bitter cold. It must be exquisitely
sensitive to its surroundings: when the outside Step 1
temperature rises, blood flow through the skin must
increase and sweat glands must secrete liquid whose
Representation of uncertainty Alternative Rule
evaporation will keep the inner temperature from also on the set of basic events
rising; when the temperature dips, vessels must
constrict to conserve body heat [2].
Some expert systems for diagnosing skin
infections have been developed which were expert
system for differential diagnosis of Step 2 Step 3
erythemato-squamous infections incorporating
decisions made by three classification algorithms:
nearest neighbor classifier, naive Bayesian classifier Merging uncertain information Conclusion
and voting feature intervals-5 [3], expert system
achieved with the import of certain medical cases [4]
and to help dermatologists in diagnosing some of the
skin infections [5]. Actually, according to researchers Figure 1. The process for dealing with uncertainty
knowledge, Dempster-Shafer theory of evidence has in Skin Infection Expert System
never been used for built an expert system for skin
infection. In step 1, an expert provides uncertain knowledge in the
The remainder is organized as follows. The form of rules with the possibility. These rules are
uncertainty in expert system is briefly reviewed in probability values. In step 2, the uncertain knowledge
Section 2. Section 3 is detailed the Dempster Shafer on a set of basic events can be directly used to draw
Theory. Skin Infection Expert System is detailed in conclusions in simple cases (step 3). However, in many
Section 4. The implementation results are presented in cases the various events associated with each other.
Section 5, and conclusion is concluded in Section 6. Therefore, it is necessary to combine the information
contained in step 1 into the global value system. In step
3, the goal is knowledge-based systems draw
II. UNCERTAINTY IN EXPERT SYSTEM conclusions. It is derived from uncertain knowledge in
steps 1 and 2, and is usually implemented by an
The construction of expert and other intelligent
inference engine. Working with the inference engine,
computer systems requires sophisticated mechanism for
representing and reasoning with uncertain information.

2. the expert can adjust the input that they enter in Step 1
after displaying the results in steps 2 and 3.
Start
III. DEMPSTER-SHAFER THEORY
The Dempster-Shafer theory was first introduced by
Dempster [7] and then extended by shafer [8], but the Yes Input
kind of reasoning the theory uses can be found as far Symptoms m1 (B) m2 (C)
back as the seventeenth century. This theory is actually
an extension to classic probabilistic uncertainty
modeling. Whereas the Bayesian theory requires No
probabilities for each question of interest, belief
functions allow us to base degrees of belief for on m (A) = 1
question on probabilities for a related question. The
advantages of the Dempster-Shafer theory as follows:
1. It has the ability to model information in a flexible
way without requiring a probability to be assigned End
to each element in a set,
2. It provides a convenient and simple mechanism Figure 2. Flowchart of Skin Infection Expert System
(Dempster's combination rule) for combining two or
more pieces of evidence under certain conditions.
3. It can model ignorance explicitly. A. Knowledge acquisition
4. Rejection of the law of additivity for belief in
disjoint propositions. In the present work, knowledge has been obtained
The Dempster Shafer theory provides a rule to from two sources. We acquired textual information
combine evidences from independent observers and into from literature such as extension booklets, reports,
a single and more informative hint. Evidence theory is papers, etc., related to the avian influenza diseases. The
based on belief function and plausible reasoning. First printed material allowed became familiar with the
of all, we must define a frame of discernment, indicated subject and a more effective communication with the
by the sign Θ . The sign 2Θ indicates the set composed experts. Most knowledge was acquired from the experts
of all the subset generated by the frame of discernment. using conventional interviewing techniques. The
For a hypothesis set, denoted by A, m(A)→[0,1]. interview methods permitted a better understanding of
m (Ø) = 0 the problem and its further representation. This
knowledge was provided by experts on skin infection.
(1) Unstructured and structured interviews were used. The
unstructured interviews were used to define the familiar
∅ is the sign of an empty set. The function m is the tasks involved in the process of identification, to obtain
basic probability assignment. Dempster's rule of an initial understanding of the range of complications
combination combines two independent sets of mass involved, and to define specific problems for later
assignments. discussion. The questions were more or less
(m1 ⊕ m2) (∅) = 0 (2) spontaneous and notes were taken on discussion. These
methods were complemented with structured interviews.
(3) In the structured interviews, we revised and discussed in
depth familiar tasks to clarify questions.
Where
B. Knowledge Base
(4) A critical aspect of building an expert system is
formulating the scope of the problem and gleaning from
m(A), m1(B), m2(C)  [0,1], A # Ø the source expert the domain information needed to
solve the problem. The reliability of an expert system
Skin Infection Expert System using depends on the quality of knowledge contained in the
Dempster-Shafer theory in the decision support process. knowledge base. In this research, we built a Skin
Flowchart of Skin Infection Expert System shown in Infection Expert System and we describe five symptoms
Figure 2. which include blister, itch, scaly skin, fever, and pain in
the rash. Knowledge base of which input on the testing
of this expert system can be seen in the Table I.

3. Table 1. Knowledge Base 0.56  0.24
m3 {CE} =  0.8
1 0
0.14
m3 {CE, DEG, I, PR}=  0.14
1 0
0.06
m3 {Θ} =  0.06
1 0
3. Symptom 3- Scaly Skin
Scaly skin is the symptom of Creeping Eruption
(CE), Dermatitis Eksfoliatif Generalisata (DEG), and
Pitriasis Rosea (PR) with a bpa of 0.6, so that:
m4 {CE, DEG, PR} = 0.6
m4 { Θ} = 1 – 0.6 = 0.4
We calculate the new bpa for some combinations
(m5). Combination rules for the m5 can be seen in the
C. Inference Engine table 3.
The inference engine is charged with the role of
Table 3. Combination of Symptom 1, Symptom 2 and
performing and controlling inference on the knowledge
Symptom 3
base. Specific features of the inference engine depend
on the knowledge representation scheme used for the
knowledge base. Because the most common
representation scheme is production rules, this inference
engine will be exemplified. The following will be
shown the inference engine using Dempster-Shafer
Theory to diagnose skin infection. 0.48  0.32
m5 {CE} =  0.8
Symptoms: 1 0
1. Blister
2. Itch 0.084  0.036
m5 {CE, DEG, PR} =  0.12
3. Scaly skin 1 0
4. Fever
5. Pain in the rash 0.056
m5 {CE, DEG, I, PR} =  0.056
1 0
1. Symptom 1 - Blister 0.024
m5 {Θ} =  0.024
Blister is a symptom of Creeping Eruption (CE). 1 0
The measures of uncertainty, taken collectively are
known in Dempster-Shafer Theory terminology as a 4. Symptom 4 - Fever
``basic probability assignment'' (bpa ). Hence we have a Fever is a symptom Erisipelas (E), Dermatitis
bpa, say m1 of 0.8 given to the focal element {CE } in Eksfoliatif Generalisata (DEG), and Nekrolisis
example, m1({CE }) = 0.8, since we know nothing Epidermal Toksika (NET), so that:
about the remaining probability it is allocated to the m6 (E, DEG, NET) = 0.4
whole of the frame of the discernment in example, m6 (Θ) = 1 – 0.4 = 0.6
m1({CE}) = 0.2, so: We calculate the new bpa value for each subset with
m1 {CE} = 0.8 bpa m7 as seen in the table 4.
m1 {Θ} = 1 – 0.8 = 0.2
Table 4. Combination of Symptom 1, Symptom 2, Symptom 3
2. Symptom 2 - Itch and Symptom 4
Itch is the symptom of Creeping Eruption (CE),
Dermatitis Eksfoliatif Generalisata (DEG), Impetigo (I),
and Pitriasis Rosea (PR) with a bpa of 0.7, so that:
m2 {CE, DEG, I, PR} = 0.7
m2 { Θ} = 1 – 0.7 = 0.3
We calculate the new bpa for some combinations 0.48
m7 (CE) =  0.705
(m3). Combination rules for the m3 can be seen in the 1  0.32
table 2.
0.048  0.022
m7 (DEG) =  0.102
Table 2. Combination of Symptom 1 and Symptom 2 1  0.32

4. 0.072
m7 (CE, DEG, PR) =  0.105
1  0.32
0.033
m7 (CE, DEG, I, PR) =  0.048
1  0.32
0.009
m7 (DEG, E, NET) =  0.013
1  0.32
0.014
m7 (Θ) =  0.021
1  0.32
5. Symptom 5 – Pain in the Rash
Pain in the rash is symptom of Erisipelas (E) with a
bpa of 0.3, so that:
m8 {E} = 0.3
Figure 4. Graphic of detection
m8 {Θ} = 1 - 0.3 = 0.7
We calculate the new bpa value for each subset with
Figure 4 shows the graphic of detection, we got the
bpa m9 as seen in the table 5.
highest basic probability assignment is CE (Creeping
Eruption) that is equal to 0.695 which show from the
Table 5. Combination of Symptom 1, Symptom 2,
last calculation of Dempster-Shafer on symptom 5
Symptom 3, Symptom 4 and Symptom 5
which means the possibility of a temporary diseases
with symptoms of Blister, Itch, scaly skin, fever, pain in
the rash is the Creeping Eruption.
D. User Interface
A user interface is the method by which the expert
system interacts with a user. The input or output
0.494 interface defines the way in which the expert system
m9 (CE) =  0.695
1  (0.212 0.031  0.032  0.014) interacts with the user and other systems such as
databases. Interfaces are graphical with screen displays,
0.071 windowing, and mouse control. They receive input from
m9 (DEG) =  0.099
1  (0.212 0.031  0.032  0.014) the user and display output to the user The user
interface development of Skin Infection Expert System
0.004  0.006 to begin with designing web pages and designing a
m9 (E) =  0.014
1  (0.212 0.031  0.032  0.014) web-based applications. Designing web pages using
0.074 PHP, the web page then connected to the MySQL
m9 (CE, DEG, PR) = 1  (0.212 0.031  0.032  0.014)  0.104 database, after that designing a web-based applications
which used to access the network database. The
relationship between applications with network
0.034
m9 (CE, DEG, I, PR) =  0.048 database has shaped overall applications to manage
1  (0.212 0.031  0.032  0.014)
information to be displayed.
0.009
m9 (DEG, E, NET) =  0.048
1  (0.212 0.031  0.032  0.014)
6. IMPLEMENTATION
0.015
m9 (Θ) =  0.021 The following will be shown the working process of
1  (0.212 0.031  0.032  0.014) expert system in diagnosing a case. The consultation
process begins with selecting symptoms found on the
The most highly bpa value is the m9 (CE) that is list of symptoms. In the cases tested, a known
equal to 0.695 which means the possibility of a symptoms are blister, itch, scaly skin, fever, pain in the
temporary infections with symptoms of Blister, Itch, rash. The consultation process can be seen in Figure 5.
scaly skin, fever, pain in the rash is the Creeping In this paper we use 5 symptoms as major symptoms
Eruption. Figure 4 shows graphic of identification. are blister, itch, scaly skin, fever, pain in the rash.

5. used some of mathematical expressions, gave them a
different interpretation: each piece of evidence (finding)
may support a subset containing several hypotheses.
This is a generalization of the pure probabilistic
framework in which every finding corresponds to a
value of a variable (a single hypothesis). In this research,
Skin Infection Expert System has been successfully
detecting skin infections and displaying the result of
identification process. This research can be an
alternative in addition to direct consultation with the
skin disease doctor and to find out quickly of skin
infections problems which can reduce serious economic
and social consequences as well as health implications.
Figure 5. The selection of symptoms REFERENCES
[1]. T. Igarashi, K. Nishino, and S. K. Nayar, The Appearance of
In the case of blister, itch, scaly skin, fever, and pain in Human Skin, Department of Computer Science Columbia
the rash, the result of consultation is creeping eruption University New York, USA, Technical Report: CUCS-024-05,
with the density value 0.695. Figure 6 shows the result 2005.
of consultation. [2]. T. A. Grossbart, C. Sherman, Skin Deep, Health Press NA Inc.,
Albuquerque, New Mexico, 2009.
[3]. Expert Systems with Applications 18, 43–49, 2000.
[4]. S. Karagiannis, A. I. Dounis, T. Chalastras, P. Tiropanis, and
D. Papachristos, Design of Expert System for Search Allergy
and Selection of the Skin Tests using CLIPS, World Academy
of Science, Engineering and Technology 31, 2007.
[5]. S.S.A. Naser, A.N. Akkila, A Proposed Expert System for Skin
Infections Diagnosis, Journal of Applied Sciences Research,
4(12): 1682-1693, 2008.
[6]. R. Yager, Reasoning with Uncertainty for Expert Systems,
International Joint Conference on Artificial Intelligence, 1985.
[7]. A. P. Dempster, A Generalization of Bayesian inference,
Journal of the Royal Statistical Society, 205 – 247, 1968.
[8]. G. Shafer, A Mathematical Theory of Evidence, Princeton
University Press, New Jersey, 1976.
Figure 6. The result of consultation
7. CONCLUSION
Detection of skin infections can be performed using
Dempster-Shafer Theory. In this paper we describe five
symptoms as major symptoms are Blister, Itch, Scaly
skin, fever, and pain in the rash of skin infections
dermatitis eksfoliatif generalisata, impetigo, pitriasis
rosea, erisipelas, and nekrolisis epidermal toksika. The
simplest possible method for using probabilities to
quantify the uncertainty in a database is that of
attaching a probability to every member of a relation,
and to use these values to provide the probability that a
particular value is the correct answer to a particular
query. An expert in providing knowledge is uncertain in
the form of rules with the possibility, the rules are
probability value. The knowledge is uncertain in the
collection of basic events can be directly used to draw
conclusions in simple cases, however, in many cases the
various events associated with each other. Knowledge
based is to draw conclusions, it is derived from
uncertain knowledge. Reasoning under uncertainty that