Skin Infection Detection using Dempster-Shafer Theory
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Skin Infection Detection using Dempster-Shafer Theory

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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 ...

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.

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Skin Infection Detection using Dempster-Shafer Theory Skin Infection Detection using Dempster-Shafer Theory Document Transcript

  • Skin Infection Detection using Dempster-Shafer Theory Andino Maseleno, Md. Mahmud HasanAbstract— Skin infections still remain common in many rural communities in developing countries, with serious economic andsocial consequences as well as health implications. Directly or indirectly, skin infections are responsible for much disability (andloss of economic potential), disfigurement, and distress due to symptoms such as itching or pain. In this research, we built a SkinInfection Expert System for detecting skin infections and displaying the result of detection process. We describe five symptoms asmajor symptoms which include blister, itch, scaly skin, fever, and pain in the rash. Dempster-Shafer theory to quantify the degreeof belief as inference engine in expert system, our approach uses Dempster-Shafer theory to combine beliefs under conditions ofuncertainty 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 inlargest organ in terms of both weight and surface area. situations where the value of a variable can only beIt has an area of approximately 16,000 cm2 for an adult narrowed down to a set of values one of which is theand represents about 8% of the body weight. Skin has a actual value of the variable. The second kind ofvery complex structure that consists of many uncertainty is related to situations in which there existscomponents. Cells, fibers and other components make uncertainty as to satisfaction of a predicate by anup several different layers that give skin a multi-layered element. This is manifested by concepts which havestructure. Veins, capillaries and nerves form vast imprecise or gray boundaries. A very powerful tool fornetworks inside this structure [1]. What makes the skin handling this type of uncertainty which also handles theunique among organs is its exposed position up against first type of uncertainty is the fuzzy set. The third typethe outside world. Other body organs can function only of uncertainty is related to situations in which the valuein a controlled, protected environment where the of a variable assumes can be modeled by thetemperature never varies far from 98.6 degrees performance of a random experiment [6]. Figure 1Fahrenheit. The skin maintains this environment, and to shows the process for dealing with uncertainty in Skindo so, it must be able to take on temperatures ranging Infection Expert System.from dry desert heat to bitter cold. It must be exquisitelysensitive to its surroundings: when the outside Step 1temperature rises, blood flow through the skin mustincrease and sweat glands must secrete liquid whose Representation of uncertainty Alternative Ruleevaporation will keep the inner temperature from also on the set of basic eventsrising; when the temperature dips, vessels mustconstrict to conserve body heat [2]. Some expert systems for diagnosing skininfections have been developed which were expertsystem for differential diagnosis of Step 2 Step 3erythemato-squamous infections incorporatingdecisions made by three classification algorithms:nearest neighbor classifier, naive Bayesian classifier Merging uncertain information Conclusionand voting feature intervals-5 [3], expert systemachieved with the import of certain medical cases [4]and to help dermatologists in diagnosing some of theskin infections [5]. Actually, according to researchers Figure 1. The process for dealing with uncertaintyknowledge, Dempster-Shafer theory of evidence has in Skin Infection Expert Systemnever been used for built an expert system for skininfection. 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 areuncertainty in expert system is briefly reviewed in probability values. In step 2, the uncertain knowledgeSection 2. Section 3 is detailed the Dempster Shafer on a set of basic events can be directly used to drawTheory. Skin Infection Expert System is detailed in conclusions in simple cases (step 3). However, in manySection 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 anThe construction of expert and other intelligent inference engine. Working with the inference engine,computer systems requires sophisticated mechanism forrepresenting and reasoning with uncertain information.
  • the expert can adjust the input that they enter in Step 1after displaying the results in steps 2 and 3. Start III. DEMPSTER-SHAFER THEORY The Dempster-Shafer theory was first introduced byDempster [7] and then extended by shafer [8], but the Yes Inputkind of reasoning the theory uses can be found as far Symptoms m1 (B) m2 (C)back as the seventeenth century. This theory is actuallyan extension to classic probabilistic uncertaintymodeling. Whereas the Bayesian theory requires Noprobabilities for each question of interest, belieffunctions allow us to base degrees of belief for on m (A) = 1question on probabilities for a related question. Theadvantages 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 (Dempsters combination rule) for combining two or more pieces of evidence under certain conditions.3. It can model ignorance explicitly. A. Knowledge acquisition4. 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 informationcombine 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. Thebased on belief function and plausible reasoning. First printed material allowed became familiar with theof all, we must define a frame of discernment, indicated subject and a more effective communication with theby the sign Θ . The sign 2Θ indicates the set composed experts. Most knowledge was acquired from the expertsof all the subset generated by the frame of discernment. using conventional interviewing techniques. TheFor 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 obtainbasic probability assignment. Dempsters rule of an initial understanding of the range of complicationscombination combines two independent sets of mass involved, and to define specific problems for laterassignments. 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 theDempster-Shafer theory in the decision support process. knowledge base. In this research, we built a SkinFlowchart of Skin Infection Expert System shown in Infection Expert System and we describe five symptomsFigure 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.
  • 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 andperforming and controlling inference on the knowledge Symptom 3base. Specific features of the inference engine dependon the knowledge representation scheme used for theknowledge base. Because the most commonrepresentation scheme is production rules, this inferenceengine will be exemplified. The following will beshown the inference engine using Dempster-ShaferTheory to diagnose skin infection. 0.48  0.32 m5 {CE} =  0.8Symptoms: 1 01. Blister2. Itch 0.084  0.036 m5 {CE, DEG, PR} =  0.123. Scaly skin 1 04. Fever5. 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 0The measures of uncertainty, taken collectively areknown 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), Dermatitisbpa, say m1 of 0.8 given to the focal element {CE } in Eksfoliatif Generalisata (DEG), and Nekrolisisexample, 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) = 0.4whole of the frame of the discernment in example, m6 (Θ) = 1 – 0.4 = 0.6m1({CE}) = 0.2, so: We calculate the new bpa value for each subset withm1 {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} = 0.7m2 { 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  0.32table 2. 0.048  0.022 m7 (DEG) =  0.102 Table 2. Combination of Symptom 1 and Symptom 2 1  0.32 View slide
  • 0.072m7 (CE, DEG, PR) =  0.105 1  0.32 0.033m7 (CE, DEG, I, PR) =  0.048 1  0.32 0.009m7 (DEG, E, NET) =  0.013 1  0.32 0.014m7 (Θ) =  0.021 1  0.32 5. Symptom 5 – Pain in the Rash Pain in the rash is symptom of Erisipelas (E) with abpa of 0.3, so that:m8 {E} = 0.3 Figure 4. Graphic of detectionm8 {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 thebpa 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 systemm9 (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 fromm9 (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 am9 (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 MySQLm9 (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 0.034m9 (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. 0.009m9 (DEG, E, NET) =  0.048 1  (0.212 0.031  0.032  0.014) 6. IMPLEMENTATION 0.015m9 (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 knownequal to 0.695 which means the possibility of a symptoms are blister, itch, scaly skin, fever, pain in thetemporary 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 symptomsEruption. Figure 4 shows graphic of identification. are blister, itch, scaly skin, fever, pain in the rash. View slide
  • 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 ofIn the case of blister, itch, scaly skin, fever, and pain in Human Skin, Department of Computer Science Columbiathe 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 usingDempster-Shafer Theory. In this paper we describe fivesymptoms as major symptoms are Blister, Itch, Scalyskin, fever, and pain in the rash of skin infectionsdermatitis eksfoliatif generalisata, impetigo, pitriasisrosea, erisipelas, and nekrolisis epidermal toksika. Thesimplest possible method for using probabilities toquantify the uncertainty in a database is that ofattaching a probability to every member of a relation,and to use these values to provide the probability that aparticular value is the correct answer to a particularquery. An expert in providing knowledge is uncertain inthe form of rules with the possibility, the rules areprobability value. The knowledge is uncertain in thecollection of basic events can be directly used to drawconclusions in simple cases, however, in many cases thevarious events associated with each other. Knowledgebased is to draw conclusions, it is derived fromuncertain knowledge. Reasoning under uncertainty that