A Belef Rule-Based (BRB) Decson Support System for Assessng Clncal Asthma Suspcon Mohammad Shahadat Hossan a, Md. Emran Hossan b, Md. Safuddn Khald c, Mohammad A. Haque d a, b Department of Computer Scence and Engneerng, Unversty of Chttagong, Bangladesh c Department of earnng and Phlosophy, Aalborg Unversty, Denmar d Department of Archtecture, Desgn and Meda Technology, Aalborg Unversty, Denmar Abstract Asthma s a common chronc dsease that affects mllons of people around the world. The most common sgns and symptoms of asthma are cough, breathlessness, wheeze, chest tghtness and respratory rate. They cannot be measured accurately snce they consst of varous types of uncertanty such as vagueness, mprecson, randomness, gnorance and ncompleteness. Consequently, tradtonal dsease dagnoss, whch s performed by a physcan, cannot delver accurate results. Therefore, ths paper presents the desgn, development and applcaton of a decson support system for assessng asthma under condtons of uncertanty. The Belef Rule-Based Inference Methodology Usng the Evdental Reasonng (RIMER) approach was adopted to develop ths expert system, whch s named the Belef Rule-Based Expert System (BRBES). The system can handle varous types of uncertanty n nowledge representaton and nference procedures. The nowledge base of ths system was constructed by usng real patent data and expert opnon. Practcal case studes were used to valdate the system. The system-generated results are more effectve and relable n terms of accuracy than the results generated by a manual system. Keywords: Belef Rule Base; Uncertanty; RIMER; Asthma Dagnoss; Asthma Suspcon; Decson Support System; Inference Introducton Asthma s a condton that affects the smaller arways of the lungs [1]. It s caused by nflammaton of the arways. The nflammaton rrtates the muscles around the arways and causes them to constrct. Ths causes narrowng of the arways. It s more dffcult for ar to get n and out of the lungs. Ths leads to wheezng and breathlessness. When arways become nflamed and fll wth mucus and the smooth muscles around the arways constrct, then chest tghtness may be experenced. Asthma patents may cough because of the rrtaton nsde the arways and the body s attempt to clean out the thc mucus. The respratory rate s defned as the number of breaths a person taes n one mnute. Durng an asthma attac, the respratory rate often ncreases. The normal respratory rate vares for dfferent age groups, such as 30-60 breaths per mnute for newborns and 12-18 breaths per mnute for adults. Fgure 1 llustrates arway nflammaton and a cross-secton of the arways durng an asthma attac n the lungs and arways. Fgure 1(A) shows the locaton of the lungs and arways n the body, Fgure 1(B) llustrates the cross-secton of a normal arway, and Fgure 1(C) depcts a cross-secton of an arway durng an asthma attac. Fgure 1- Mechansm of asthma 1 Asthma may occur at any age but s most common n chldren [1]. It may also be due to heredtary factors [2]. The process for asthma suspcon or dagnoss conssts of observng a patent s sgns and symptoms [3]. However, ths asthma suspcon process contans errors because the sgns and symptoms cannot be measured wth 100% certanty. There are causal relatonshps among the sgns and symptoms of asthma. These causal relatonshps can be represented by the If-Then rule. An If-Then rule has an antecedent and a consequent part. The antecedent part taes nput data whle the consequent part shows the acton to be taen. For example, sgns and symptoms data are nput data that s the antecedent part of varous rules. Input data n a rule can dffer n type and scale such as qualtatve, quanttatve etc. For example, a patent s breathlessness may be severe, moderate, mld or normal. Ths degree of llness s presented by qualtatve data. These data are expressed as a lngustc term by patents and contans uncertanty due to vagueness and mprecson. However, a patent can have a respraton problem that can be wthn the range or out of the range. Ths can be measured wth an optcal breath rate sensor [4] that produces numerc or quanttatve data. The result may be ncorrect due to mshandlng of the nstrument, gnorance and randomness. Sometmes, a symptom such as 1 Source: http://www.nhlb.nh.gov/health/healthtopcs/topcs/asthma/ Scandnavan Conference on Health Informatcs, August 21-22, 2014, Grmstad, orway 83
cough may be hdden by patents, whch arses out of gnorance and ncompleteness. Therefore, varous types of uncertanty can be assocated wth nput data such as ambguty, vagueness, mprecson, gnorance etc. Thus, the consequent part of the If-Then rule may contan uncertanty. For example, the ntal belef degree can be mperfect snce t contans ncompleteness and gnorance. Some expert systems for dagnosng asthma have been reported n the lterature. A fuzzy rule-based expert system for assessng the severty of asthma was presented by [5]. The assessment of the severty of asthma by an expert system s llustrated by [6]. A clncal support system [7] was also developed to assess asthma. However, these systems cannot handle dfferent types of uncertanty. Dagnosng asthma s an example of a complex problem that can be handled by an expert system. An expert system has two components: the nowledge base and the nference engne. The nowledge base can be constructed wth proportonal logc (P), frst-order logc (FO) or fuzzy logc (F) [8,9]. Reasonng mechansms such as forward channg and bacward channg are used to develop the nference engne [10]. P and FO are not equpped to capture uncertanty. However, F can handle uncertanty due to vagueness and ambguty. However, F cannot handle other types of uncertanty such as gnorance and ncompleteness that may exst n sgns and symptoms of asthma. Therefore, a nowledge base that can handle all types of uncertanty that exst wth dagnosng asthma must be developed. A relevant nference mechansm must also be adopted. Uncertan nowledge n dagnosng asthma must be processed by usng a refned nowledge base and an nference mechansm. A recently developed Belef Rule-Based Inference Methodology Usng the Evdental Reasonng (RIMER) approach [11,12] was used to desgn and develop the proposed decson support system. Uncertanty can be addressed by ths methodology. Ths methodology conssts of the Belef Rule Base (BRB) and the Evdental Reasonng (ER) algorthm. In RIMER, a rule base s desgned wth belef degrees embedded n all possble consequents of a rule. Inference n such a rule base s mplemented usng the evdental reasonng approach that can handle dfferent types and degrees of uncertanty n sgns and symptoms. The rest of the paper s organzed as follows. The next secton provdes an overvew of the RIMER methodology. Then the stem archtecture, desgn and mplementaton of the proposed BRBES are dscussed. Expermental results and dscussons are then presented. A concluson s ncluded to summarze the contrbuton. Overvew of RIMER Methodology The RIMER approach conssts of two components [10]. They are BRB to act as the nowledge base and ER to act as an nference engne. Doman Knowledge Representaton usng BRB Belef rules are the ey elements of a BRB, whch nclude belef degree. It s an extended form of tradtonal If-Then rules. A belef rule conssts of an antecedent part and a consequent part. The antecedent attrbute taes referental values, and each consequent s assocated wth belef degrees [12]. The nowledge representaton parameters are rule weghts, antecedent attrbute weghts and belef degrees n consequents, whch can handle uncertanty. A belef rule can be defned as follows: P s A P s A... P s A IF 1 1 2 2 T T R : (1) THE C 1, B1, C 2, B2,..., C, B R : 0, 1 1 Wth a rule weght and attrbutes weght 1, 2, 3,..., T where 1,...,, where P 1, P2, P3,..., P T represent the antecedent attrbutes n the - th rule. A 1,..., T and 1,..., represents one of the referental values of the -th antecedent attrbute P n the -th rule. C s one of the consequent reference values of the belef rule. 1,..., and 1,..., s the degree of belef to whch the consequent reference value C s beleved to be true. If, the -th rule sad to be complete; 1 1 number of all belef rules n the rule base. s the number of all possble consequents n the rule base. An example of a belef rule n the asthma suspcon/dagnoss BRB system prototype can be wrtten n the followng way: IFCough syesand Breathlessness s Moderateand R : Wheezes Hghand Chest tghtnesssyes Re spratory ratesun range Severe, 0.6, Moderate, 0.4, Mld, 0.0, ormal, 0.0 THE Asthma Suspcons Where Severe, 0.6, Moderate, 0.4, Mld, 0.0, ormal, 0.0 s a belef dstrbuton assocated wth asthma consequents of the belef rule as represented n (2). The belef dstrbuton states that the degree of belef assocated wth severe asthma s 60%, 40% degree of belef s assocated wth moderate asthma, 0% degree of belef s assocated wth mld asthma and 0% degree of belef s assocated wth normal asthma. Here, severe, moderate, mld and normal are the referental value of the consequent attrbute Asthma of the belef rule. In ths belef rule, the total degree of belef s (0.6+0.4+0+0) =1, and thus, the assessment s complete. Inference Procedure n BRB The nference procedure n the BRB nference system conssts of varous components such as nput transformaton, actvaton weght calculaton, belef degree update mechansm and rule aggregaton usng ER. The nput transformaton of the antecedent attrbute value dstrbutes the value of a belef degree of dfferent referental values of that antecedent. Ths s equvalent to transformng an nput nto a dstrbuton on the referental values of an antecedent attrbute by usng ther correspondng belef degrees [13]. At an nstant pont n tme, the -th value of an antecedent attrbute P can equvalently be transformed nto a dstrbuton over the referental values of that antecedent attrbute by usng ther belef degrees [11]. The -th nput value P, whch s the -th antecedent attrbute along wth ts belef degree of a rule s shown below by (3). The belef degree s assgned to the nput value by the experts. Scandnavan Conference on Health Informatcs, August 21-22, 2014, Grmstad, orway 84 and (2)
P, A,, 1,...,, 1, T H..., (3) Here, H s used to show the assessment of the belef degree assgned to the nput value of the antecedent attrbutes. In ths equaton, A (-th value) s the -th referental value of the nput P. s the belef degree to the referental value, A wth 1 0. 1, 1,..., T 1, and s the number of the ref- erental values. The nput value of an antecedent attrbute s collected from the patent or from the physcan n terms of lngustc values such as severe, moderate, mld and normal. These lngustc values are assgned a degree of belef usng expert udgment. Ths assgned degree of belef s then dstrbuted n terms of belef degree of the dfferent referental values A. There are fve nput antecedents: cough (A 1 ), breathlessness (A 2 ), wheezng (A 3 ), chest tghtness (A 4 ) and respratory rate (A 5 ). The referental values of these antecedent attrbutes consst of severe (S), moderate (Mo), mld (M) and normal (). The devsed rules are as follows: IF S valuenput value Mo valuethe (4) S value nput value Moderate, Severe 1 Moderate, Mld 0.0, ormal 0.0 S value Mo value IF Movaluenputvalue M valuethe (5) Movalue nputvalue Mld, Moderate 1 Mld, Severe 0.0, ormal 0.0 Movalue M value IF M valuenputvalue valuethe (6) Movalue nputvalue ormal, Mld 1 ormal, Severe 0.0, Moderate 0.0 Movalue M value t T t1 J t t, T, t1 1 t (8) Where 1, f P sused n defnng R t, 0, otherwse t 1,..., T Here, s the orgnal belef degree, and s the updated belef degree. If gnorance occurs, then the belef degrees are updated. For example, f the nput antecedent cough s gnored, then the ntal belef degrees are updated. The updated belef degrees are shown n Table 1. Rule Id 1 2 3 Table 1 - Belef degree update Severe D1 Moderate D2 Mld D3 ormal Intal 0.6 0 0 0.4 0 Update 0.48 0 0 0.32 0.2 ntal 0.8 0 0 0.2 0 Update 0.64 0 0 0.16 0.2 Intal 0.4 0 0 0.6 0 Update 0.32 0 0 0.48 0.2 D4 Dn In the -th rule, t s assumed that s the belef degree of one of the referental values A (whch s the element of A ) of the th nput P. Ths s called the ndvdual matchng degree. Here, can be calculated by usng (4), (5), (6) and (7). When the -th rule s actvated, the weght of actvaton of the -th rule,, s calculated by usng the flowng formula [10,11]. Here, 1 T 1 T 1 1 s the relatve weght of ' ', ' max 1,..., T (7) P, whch s used n the - th rule and s calculated by dvdng the weght of P by the maxmum weght of all antecedent attrbutes of the -th rule to normalze the value of whch means that ts value should range between 0 and 1. The combned matchng degree T ' 1, whch s calculated by usng the multplcatve aggregaton functon. If the -th rule as gven n equaton (1) s actvated, the ncompleteness of the consequent of the rule can also result from ts antecedents due to the lac of data. The orgnal belef degree n the -th consequent C of the -th rule s updated based on the actual nput nformaton n (8) as devsed n [10]. All pacet antecedents of the rules are aggregated by usng the ER approach to obtan the degree of belef of each referental value of the consequent attrbute usng the gven nput values P of the antecedent attrbutes. In ths study, ths aggregaton s carred out usng an analytcal approach, whch has been consdered snce t s more computatonally effcent than the recursve approach [12,13]. The output O(Y), consstng of the referental values of the consequent attrbutes s generated by usng the analytcal ER algorthm [14]. Ths s llustrated n equaton (9): O Y SP C,, 1,..., (9) Here, denotes the belef degree assocated wth one of the consequent reference values such as C. s calculated wth the analytcal format of the ER algorthm [11 14] as llustrated n (10). wth 1 1 1 1 1 1 1 1 1 1 (10) 1 1 1 1 1 1 1 1 The fnal output generated by ER s represented by C1, 1, C2, 2, C3, 3,..., C,, where s the fnal belef degree attached to the -th referental value C of the consequent attrbute, whch s obtaned after all actvated rules n the BRB are combned by usng ER. Ths output can be converted nto a crsp/numercal value by as- Scandnavan Conference on Health Informatcs, August 21-22, 2014, Grmstad, orway 85
sgnng a utlty score to each referental value of the consequent attrbute [7,10], as shown n (11). * H A uc (11) 1 * Where A value and u C s the utlty score of each referental value. H s the expected score expressed as a numercal BRBES for Assessng Asthma Suspcon Ths secton presents the desgn, mplementaton, nowledgebase constructon and nterface of the BRBES for dagnosng asthma. Archtecture, Desgn and Implementaton of the BRBES The system archtecture represents how ts components consstng of nput, process, and output are organzed. The system also consders the pattern of the system organzaton, nown as the archtectural style. It conssts of a user nterface layer (used to get the nput and produce system output), nference engne and nowledge base (consstng of the ntal rule-base developed usng BRB and facts ncludng sgns and symptoms of asthma). The Relaton Database Management System (RDBMS) was chosen to store data snce ths system has a flexble desgn and s portable n dfferent system envronments. PHP, whch s avalable n etbeans 6.9.1, was used to develop the user nterface. The system archtecture s shown n Fgure 2. Fgure 3- BRB framewor for asthma suspcon An example belef rule of ths BRB system as follows: R : 1 IFCoughsYesand Breathlessness s Severe Wheezes HghAD Chest tghtness syes Re spratory rates Out of range Asthma s THE AD AD S 1.00, Mo0.00, M 0.00, 0.00 In ths belef rule, the belef degrees are attached to the three referental values of the consequent attrbute. For example, havng 100 patents whose clncal nformaton such as Cough s Yes, Breathlessness s Severe, Wheeze s Hgh, Chest tghtness s Yes and Respratory rate s Out of Range. Ths means that f the clncal nformaton of the antecedent parts of the rule s evaluated wth a hgh referental value of the correspondng antecedent attrbute of the rule, then all patents are udged as severe regardng a dagnoss of asthma. Consequently, the rule can assgn ntal belef degrees to the consequents n the rule such as 1 to S, 0 to Mo, 0 to M and 0 to as shown n rule R1. BRBES Interface Fgure 2- The BRBES system archtecture Knowledge Base Constructon n BRB To construct the nowledge base for ths BRB system prototype, a BRB framewor was used by followng the Brtsh Gudelne on the Management of Asthma. In the framewor, the nput factors that determne suspcon are A 1 = Cough, A 2 = Breathlessness, A 3 = Wheeze, A 4 = Chest tghtness, A 5 = Respratory rate and A 6 = Asthma. Ths BRB conssts of only the Asthma (A 6) rule base and s depcted n Fgure 3. The rule base has fve antecedent attrbutes. The total number of rules,, s usually determned wth the followng method: T 1 J (12) Here J 1 = 2, J 2 = 4, J 3 = 3, J 4 = 2, J 5 = 2, so = (2*4*3*2*2) = 96. Thus, the entre BRB conssts of 96 belef rules as llustrated n Table 2. It s assumed that all belef rules have equal rule weght and all antecedent attrbutes have equal weght. The ntal belef base for the asthma suspcon BRB system s lsted n Table 2. A system nterface can be defned as the medum that enables nteracton between the users and the system. Fgure 4 llustrates a smple nterface of the BRBES. Here, the nput antecedent for cough s yes, breathlessness s moderate, wheezng s hgh, chest tghtness s yes and respratory rate s wthn the range. The BRB/RIMER system generates the fuzzy value of the referental value. Then the system converts the fuzzy value nto one numercal value by multplyng four utlty factors. The four reference values are 1.0 for the Severe referental value, 0.66 for the Moderate referental value, 0.33 for the Mld referental value and 0.0 for the ormal referental value. The fuzzy output of the system s Asthma (A6) :{( Severe, (23.67%)), (Moderate, (39.36%)), (Mld, (3.38%), (ormal, (33.59%))}. Severe referental value = (23.67%*1.00) = 23.67%, Moderate referental value = (39.36%*0.66) = 25.98%, Mld referental value = (3.38%*0.33) = 1.11%, and ormal referental value = (33.59%*0) = 0%. Thus, the total system output s (23.67%+25.98%+1.11%+0%) = 50.77%. The result of the system s dagnoss s 50.77% for the asthma suspcon. Ths result s shown n Fgure 4. Scandnavan Conference on Health Informatcs, August 21-22, 2014, Grmstad, orway 86
Table 2 - Belef degree update IF THE Rule ID Rule Weght Cough Breathlessness Wheezng Chest tghtness Respratory rate Asthma S Mo M R1 1 Yes o lmtaton Hgh Yes Range 0.6 0 0 0.4 R2 1 Yes o lmtaton Hgh Yes Out of range 0.8 0 0 0.2 R3 1 Yes o lmtaton Hgh o Range 0.4 0 0 0.6 R4 1 Yes o lmtaton Hgh o Out of range 0.6 0 0 0.4 R5 1 Yes o lmtaton Medum Yes Range 0.4 0.2 0 0.4 R6 1 Yes o lmtaton Medum Yes Out of range 0.6 0.2 0 0.2 R7 1 Yes o lmtaton Medum o Range 0 0.4 0 0.6......... R93 1 o Severe ow Yes Range 0.4 0 0 0.6 R94 1 o Severe ow Yes Out of range 0.5 0 0.3 0.2 R95 1 o Severe ow o Range 0.2 0 0 0.8 R96 1 o Severe ow o Out of range 0.3 0 0 0.7 Table 3- Asthma suspcon by BRBES and expert Patent ID Sgns and Symptoms Chest Cough Breathlessness Wheezng tghtness Respratory rate Expert system/brbes output Expert opnon/ physcan s opnon Benchmar/ dagnostc result P1 Yes Moderate Medum o Range 58.27% 65.0 1.0 P2 Yes Mld Medum Yes Out of range 79.48% 85.0 1.0 P3 o o lmtaton ow Yes Range 14.62% 24.0 0.0 P4 o Severe Medum o Out of range 68.67% 76.0 1.0 P5 Yes Mld Hgh Yes Out of range 85.98% 90.0 1.0 P6 o Moderate Hgh Yes Range 50.94% 57.0 1.0 P7 Yes Mld ow Yes Range 42.83% 50.0 1.0 P8 Yes Moderate Hgh Yes Out of range 90.69% 95.0 1.0 P9 o Moderate Medum Yes Range 46.76% 56.0 0.0 P10 o Severe ow Yes Range 35.28% 45.0 0.0 Results and Dscusson In ths research, leaf nodes data of the BRB were collected from patents who suffer from asthma. Then the patent data were used n the BRBES to assess asthma suspcon. Expert opnon on the asthma suspcon was also collected as shown n Table 3. If a patent has asthma, then the benchmar datum s 1; otherwse, t s 0. The data set conssts of ffty samples. For smplcty, data for only ten patents s presented n Table 3. The recever operatng characterstc (ROC) curve can help effectvely analyze the performance of the suspcon/dagnoss tests that have ordnal or contnuous results [16]. It can be used to test the results of the BRB Expert System and the manual system/expert opnon results by usng the benchmar results. The system performance can be measured by calculatng the area under the curve (AUC) [16 19]. If the AUC of the BRBES s larger than the expert opnon, then the BRBES produces more accurate and relable results. Fgure 5 shows the two ROC curves. One represents the suspcon performances of the BRB system prototype, and the other s the result of the manual system/expert opnon. The ROC curve wth a red lne n Fgure 5 llustrates the BRB system asthma dagnoss whle the curve wth green lne llustrates the manual system asthma dagnoss. The AUC for the BRB system prototype s 0.952 (95% confdence nterval = 0.960 1.012), and the AUC for the expert opnon s 0.857 (95% confdence nterval = 0.939 1.014). From the AUC of the BRBES and expert opnon, the AUC for the BRBES s greater than the Scandnavan Conference on Health Informatcs, August 21-22, 2014, Grmstad, orway 87
AUC for the expert opnon. Ths mples the results generated by the BRBES are better than the results generated by expert opnon. SPSS 16.0 was used to construct the ROC curve and to calculate the AUC of the curves. Fgure 4- BRBES nterface Fgure 5- ROC curves of asthma suspcon between the BRBES and expert opnon (manual system) The great achevement of our research s to overcome the uncertanty problem nvolved n dagnosng asthma, whch cannot be overcome wth the tradtonal rule-based system. The BRBES can handle varous types of uncertanty such as ambguty, vagueness, mprecson, gnorance etc. Concluson In ths paper, we demonstrated the development and applcaton of a BRBES to dagnose asthma based on sgns and symptoms. Ths BRBES used a methodology nown as Scandnavan Conference on Health Informatcs, August 21-22, 2014, Grmstad, orway 88
RIMER that handles varous types of uncertanty found n doman nowledge. The BRBES s a robust tool that can ad n assessng asthma suspcon. The system wll help patents assess mprovement n asthma severty as well. Ths BRBES provdes a percentage of the assessment, whch s more relable and nformatve than from a tradtonal expert s opnon that gven wthout a degree of belef that s weghted wth percentage value. Results generated by the BRBES were more relable than the tradtonal expert opnon. The system has strong potental n developng countres n Afrca and Asa, n addton to other countres, where there s a lac of healthcare resources, dagnoss machnery and expert physcans. References [1] Jeffery PK. Remodelng n Asthma and Chronc Obstructve ung Dsease. Am J Respr Crt Care Med 2001;164:S28 S38. [2] Dold S, Wst M, von Mutus E, Retmer P, Stepel E. Genetc rs for asthma, allergc rhnts, and atopc dermatts. Arch Ds Chld 1992;67:1018 22. [3] Rahman S, Hossan MS. A Belef Rule Based System Prototype for Asthma Suspcon, Khulna, Bangladesh: 2013. [4] Mathew J, Semenova Y, Farrell G. A mnature optcal breathng sensor. Bomed Opt Express 2012;3:3325. [5] Zolnoor M, Zarand MHF, Mon M, Temoran S. Fuzzy Rule-Based Expert System for Assessment Severty of Asthma. J Med Syst 2012;36:1707 17. [6] Reder H, Daures JP, Mchel C, Proudhon H, Vervloet D, Charpn D, et al. Assessment of the severty of asthma by an expert system. Descrpton and evaluaton. Am J Respr Crt Care Med 1995;151:345 52. [7] Mshra, Sngh D, Bandl MK, Sharma P. Decson Support System for Asthma (DSSA). Int J Inf Comput Technol 2013;3:549 54. [8] Angulo C, Cabestany J, Rodríguez P, Batlle M, González A, de Campos S. Fuzzy expert system for the detecton of epsodes of poor water qualty through contnuous measurement. Expert Syst Appl 2012;39:1011 20. [9] u TI, Sngonahall JH, Iyer R. Detecton of Roller Bearng Defects Usng Expert System and Fuzzy ogc. Mech Syst Sgnal Process 1996;10:595 614. [10] Russell SJ, orvg P, Davs E. Artfcal ntellgence: a modern approach. Upper Saddle Rver, J: Prentce Hall; 2010. [11] Jan-Bo Yang, Jun u, Jn Wang, How-Sng S, Hong- We Wang. Belef rule-base nference methodology usng the evdental reasonng Approach-RIMER. IEEE Trans Syst Man Cybern - Part Syst Hum 2006;36:266 85. [12] Kong G, Zu D, Yang JB. An evdence-adaptve belef rule-based clncal decson support system for clncal rs assessment n emergency care, Bonn, Germany: 2009. [13] Jan-Bo Yang, Pratyush Sen. A general mult-level evaluaton process for hybrd MADM wth uncertanty. IEEE Trans Syst Man Cybern 1994;24:1458 73. [14] Wang Y-M, Yang J-B, Xu D-. Envronmental mpact assessment usng the evdental reasonng approach. Eur J Oper Res 2006;174:1885 913. [15] Jan-Bo Yang, Jun u, Jn Wang, Guo-Png u, Hong- We Wang. An optmal learnng method for constructng belef rule bases. vol. 1, IEEE; 2004, p. 994 9. [16] Body R. Clncal decson rules to enable excluson of acute coronary syndromes n the emergency department. Doctoral Thess. Manchester Metropoltan Unversty, 2009. [17] Salsa H, Freylch V. Web-bootstrap estmate of area under ROC curve. Aust J Stat 2006;35:325 30. [18] Metz CE. Basc prncples of ROC analyss. Semn ucl Med 1978;8:283 98. [19] Hanley JA. The Robustness of the Bnormal Assumptons Used n Fttng ROC Curves. Med Decs Mang 1988;8:197 203. Address for correspondence Mohammad Shahadat Hossan, Department of Computer Scence and Engneerng, Unversty of Chttagong, Chttagong-4331, Bangladesh. Emal: hossan_ms@hotmal.com Scandnavan Conference on Health Informatcs, August 21-22, 2014, Grmstad, orway 89