Apply Gaussian Distribution and RBF Neural Network to Diagnosis and Prescription High Blood Pressure Disease in Oriental Medicine

Transcription

1 Apply Gaussa Dstrbuto ad BF Neural Network to Dagoss ad Prescrpto Hgh Blood Pressure Dsease retal Medce Cao Thag Graduate chool of cece ad Egeerg, tsumeka Uversty, Japa Yukobu Hosho College of Iformato cece ad Egeerg, tsumeka Uversty, Japa Nguye Hoag Phuog Iformato Techology Ceter, Mstry of Health of Vetam Erc W Cooper College of Iformato cece ad Egeerg, tsumeka Uversty, Japa cooper@srtsumeacp Katsuar Kame College of Iformato cece ad Egeerg, tsumeka Uversty, Japa kame@crtsumeacp Abstract I ths paper, we preset a decso support system for dagoss ad prescrpto hgh blood pressure dsease wth oretal medce Iputs to the system are severtes of symptoms observed o patets ad outputs from the system are a dagoss of dsease states ad oretal treatmet herbal prescrptos Frst, usg rule ferece wth a Gaussa dstrbuto, the most serous dsease state of hgh blood pressure whch the patet appears to be fected s determed Next, a oretal prescrpto wrtte sutable herbs wth reasoable amouts for treatg the fected dsease state s gve by BF eural etworks Fally, we show some expermets ad ther evaluatos, ad the descrbe our future works Keywords: Decso upport ystem, retal Medce, Gaussa Dstrbuto, BF Neural Networks Itroducto I the oretal coutres, especally Cha ad Vetam, there are two therapeutc ways for treatg dseases: retal Medce (M), whch has bee used for thousads years, ad Wester Medce (WM), whch was troduced at the ed of the 9 th cetury WM s based o atbotc, compoud drugs ad advaced equpmet whle M reles o acupucture, herbal remedes ad accumulated popular expereces Accordg to M, the orgas of people s bodes are closely related Whe a dsease s fected, some abormal symptoms wll appear the exteral orgas The severtes of the fected dseases are expressed by severtes of observed symptoms Whe dagosg, M physcas have to make full use of ther bras ad sesory orgas for clcal observatos ad udgmets To get a rght dagoss ad a sutable treatmet prescrpto, clcal expereces of the physcas are extremely mportat Accurate dagoses ad treatmet prescrptos have a mportat role treatg dseases Applyg sutable computg models ad the buldg successful Decso upport ystem (D) based o kowledge from sklled M physcas ca help to moderate subectve evaluatos dagoss ad prescrpto processes, ad cosequetly t wll help to provde the rght treatmets to the rght patets ad to mprove the qualty of health care servces ule Iferece wth Gaussa Dstrbuto for Dagoss ad BF Neural Network for Prescrpto Accordg M, hgh blood pressure dsease has 7 clcal symptoms, 6 specfed states ad each state s cured by stadard herbal prescrptos uch prescrptos are easly foud medcal text books or M referece books For a cocrete patet, some herbs the stadard prescrpto are too strog whle some are weak Because of herbal propertes

2 ad the body status of each patet, smply reducg or creasg the herbal amouts sometmes ca ot moderate the effects of the strog or weak herbs Thus the total effect of the herbs the gredet s ofte moderated by chagg ther amouts, together wth addg some addtoal herbs These herbal adustmets are ofte based o the severtes of observed symptoms o the patet [] ly expereced physcas ca gve patets sutable prescrptos wth reasoable adustmets Catalyzg herbs help patets' orgasms to easly absorb herbal effects Treatg herbs drectly cure the fected symptoms TEATMENT PECIPTI N Treatg herb x Treatg herb x Catalyzg herb y Catalyzg herb y Addtoal herb z gram Addtoal herb z gram Addtoal herbs are added to moderate effects of gredet, ehacg the herbal the effcecy of the prescrpto Treatg ad Catalyzg herbs, wth ther stadard weghts, are lsted thestadard prescrpto the severtes of x x y y ad Catalyzg herbs are based o Adustmet of amouts of Treatg the fected dsease Fgure : A herbal treatmet prescrpto I a oretal treatmet prescrpto, there are three ma kds of herbs: Treatg, Catalyzg ad Addtoal Herbs as show Fg Treatg herbs drectly cure the fected symptoms whle catalyzg herbs help patets orgasms to easly absorb herbal effects Amouts of treatg herbs are ofte adusted by severtes of the observed symptoms whereas amouts of catalyzg herbs are ormally uchaged as the stadard prescrptos Treatg ad catalyzg herbs, wth ther stadard amouts, are lsted stadard prescrptos Depedg o the patet s body status ad the severty of dsease states, expereced doctors ofte add some addtoal herbs to the stadard prescrpto to ehace ts effcecy The processes of above dagoss ad prescrpto could be asssted wth a D model as show Fg [] oles of the fuctoal parts Fg are as follows: Kowledge Acqusto: urveys symptoms, explaatos, sample prescrptos ad mportace values of symptoms Kowledge Base: Cossts of symptoms, dsease states, ferece rules, trag data ad explaatos klled Physcas Developer Iterface Kowledge Acqusto evertes of observed symptoms ample Prescrptos Neural Networks Prescrptos BF Users User Iterface Dsease state Kowledge Base Dsease states Herbal treatmet prescrpto evertes ule Iferece ules Explaato Fgure : D for dagoss ad prescrpto retal Medce ule Iferece: Checks rules, calculates severtes ad advses the most serous dsease state BF Neural Networks: Gves prescrptos wth reasoable amouts of herbs User Iterface: btas symptoms ad ther severtes from users ad shows feretal results Developer Iterface: btas mportace values of symptoms, severtes, ad sample prescrptos from expereced doctors ad kowledge egeers Explaato: Helps users to uderstad the dagosed dseases ad explas the results ule Iferece I M, physcas usually gve herbal prescrptos based o severtes of clcal symptoms such as hgh body temperature, moderately yellow ure etc These vague expressos of symptoms make t usutable for tradtoal quattatve approaches to buld D M Fuzzy sets, kow for ther abltes to deal wth vague varables usg

3 membershp fuctos rather tha wth crsp values, have prove to be a sutable approach to resolve ths problem [3-6] However, usg fuzzy rules, a D may requre thousads of rules wth may combatos of symptoms premses to obta reasoable ferece results The large umber of rules would requre ot oly a lot of tme for developers to accomplsh the rule acqusto but also much effort for the doma experts to revse all of the rules I ths D model, we use severtes of observed symptoms ad a Gaussa dstrbuto to fer the most serous fected dsease state that the patet has The the prescrpto stage, the observed severtes wll be put to BF eural etworks to get a approprate herbal prescrpto ymptom ad rule expresso uppose that hgh blood pressure dsease has m = 7 clcal symptoms, l = 6 dsease states A dsease state s determed by clcal symptoms Let = (,,, m ) be a set of observed symptoms o a patet Let H = ( H, H,, Hl ) be a set of the dsease states Let = (,,, ) be a set of symptoms premse of rule ( =,,, l), where s geerally descrbed the followg form: IF ad ad ad THEN dsease state s H Let the followg certaty values () ad be defed: [0,] : truth value of gve by doctors whe dagosg, where = meas clearly appears o the patet, = 0 meas does ot appear o the patet, ad 0 < meas < appears o the patet wth the severty [0,] : mportace value of for the prescrpto of dsease state H gve by sklled doctors va survey advace, where = = = 0 meas affect o H, = meas symptom affectg meas () has absolutely o s the oly H, ad 0 < < affects H wth certaty factor The mportace values are troduced because dfferet symptoms affect dsease states dfferetly For a dsease state, some symptoms are more mportat tha the others whle some do ot The sum Eq () equals, whch meas the maxmum belef degree of symptoms the premse of rule s Ths sum s also a boudary, so that expereced doctor should cosder the mportace values wth ths boudary, avodg may doctors freely express the mportace of each symptom wth ther ow vew ule ferece process If a observed symptom s foud the premse of rule, actual effect of ths symptom to the premse s calculated as: = (3) Where s a product operator We apply the multplcato as the product For example, symptom observed o the patet wth severtes = 0 75 Ths symptom affects Dsease state wth the mportace value = 0 3 The actual effect of ths symptom premse of s = = = 05 If symptoms of match wth observed symptoms, the serous degree of the dsease state H s calculated as: r r = exp σ (4) r s a vector represetg the actual effect of observed symptoms premses of

4 r s the ceter vector of a Gaussa dstrbuto, represetg effects of symptoms o the dsease state H decded by r r r represets how well r closes to σ s the varace of the dstrbuto ad should be chose so that the 0 whe r r r The formula (4) meas that whe r close to r, wll close to, or the rule decdes the most serous dsease state Whe r close 0, wll close to 0, or the dsease state decded by rule s less serous Fg 3 llustrates the ferece process of rule The the most serous dsease state H * havg the largest value amog l dsease states wll be determed: * H = { = max } (5) H k k r r exp = σ Fgure 3: Iferece process of rule BF Neural Networks ad Prescrpto Neural Network (NN) s a effectve techque to help doctors to aalyze, model ad make sese of complex clcal data across a broad rage of medcal applcatos [7,8] Based o typcal sample prescrptos from expereced doctors, NN ca geeralze prescrpto rules ad relatos betwee severtes of symptoms ad herbal amouts After trag, NN ca gve sutable herbal prescrptos accordace wth the severtes of symptoms observed o the patet Archtectures of NNs wdely used are Mult-Layer Perceptro eural etwork (MLP) ad adal Bass Fucto eural etwork (BF) MLP has a good geeralzato whe trag data s eough ad cosstet, but t early ecouters over-fttg problem whe fewer or cosstet trag data are used The trag procedure of MLP s ofte slow ad t may eed to reset from the begg whe a ew trag data s added BF lears fast, ca approxmate ay fucto, ca be traed for ew cases wthout havg to redo old cases, ad ca lear wth some coflcto the trag data BF has low cdece of false postve (wthout ormalzato) or wde output coverage (wth ormalzato) The dsadvatages of BF are that t requres more hdde euros tha MLP for a good approxmato of fuctos large datasets, [9-] Typcal treatmet prescrptos ca be collected from expereced doctors However, the umber of the collected prescrptos s ofte ot large, ad there are also some vald prescrptos that are coflct wth each other The prescrptos usually ca ot be quckly collected because the process of prescrpto acqusto s accumulated over tme For these reasos, we used BF for the herbal prescrpto descrbed above Traed by herbal treatmet prescrptos collected from sklled M doctors, BFs are used to gve herbal prescrptos wth reasoable amouts Each dsease state uses a dedcated BF as show Fg 4 Iputs to BF are severtes of the state-specfed symptoms ad outputs are coeffcets of herbal amouts for treatg the dsease state Each bass fucto of BF ca be regarded as beg cetered o a vector of a severty group of observed symptoms For the puts, there are two types of symptoms The frst type s assocated wth Boolea values: Yes (true coded by ) ad No (false coded by 0) bserved severtes the secod type are assocated wth 5 lgustc values compay wth certaty tervals: o (000), slghtly (05), moderately (050), relatvely (075), clearly (00) For the outputs, the total umber of herbs a stadard prescrpto s ofte from 9 to The umber of addtoal herbs for a stadard prescrpto s ofte from to 3 The umber of observed symptoms used to adust herbs a stadard prescrpto s ofte from 5 to 0 I trag data for the BFs, symptoms affectg herbal adustmets are used for puts ad all

5 of the Treatg, Catalyzg ad Addtoal herbs are used for outputs Depedg o each dsease state, the amouts of herbs prescrptos ofte vary from 3 to 0 grams The error the adusted amouts of a herb accepted by doctors s usually 05 gram for small amouts, gram for medum amouts ad gram large amouts Amouts of herbs trag data are ormalzed as coeffcets [0,] The coeffcet ck of amouts of herb k trag data, ad the actual amout W of herb k the prescrpto results s calculated as: T * ck Wk /W (6) P * Wk = ck W (7) where s amout of herb k trag T Wk data, ad W * s the maxmum amout of a herb the prescrpto For the same dsease state, prescrptos by dfferet doctors mght ot look smlar because some doctors use some herbs but others prefer equvalet herbs that also gve the same effects but come dfferet amouts To avod usg may equvalet herbs for the same prescrptos trag data, lsts of herbs the stadard prescrptos from M text books are used ad clarfed by expereced doctors Bas P k pressure dsease, wth typcal severtes ad herbal adustmets, ad the put these prescrptos to BF etworks The umber of trag data s 80% ad the umber of testg data s 0% Basc fucto ceters are radomly selected from trag data sets, ad varace σ s the average dstace betwee the basc fucto ceters After trag, all of prescrpto rules ad relatos betwee severtes of symptoms ad herbal amouts were well leart by BF, wth a accuracy of 0 - mea square error wth both trag ad testg data (equvalet to mea error of 0 gram for each herb) I case of ukow puts, system shows the graph of the serous degree of fected dsease, recommeds the most proper state whch the patet seems to be fected, the shows a advsed prescrpto wth approprate herbal amouts by BF Fg 5 shows the serous degrees of fected states of hgh blood pressure dsease, the varace σ = 0 05 the dstrbuto of the serous degree s chose after some expermets Expereced physcas have also cofrmed that t was easy to uderstad the serous of the fected dsease states va the graph INPUT evertes of specfc symptoms [0,] r r r x Φ ( x) = exp σ w 0 p Φ Φ Φ q w k w 0 w 0k k ck = w kφ + w0 k p c p = wpφ + w0 p UTPUT Coeffcets of amouts of herbs [0,] c = w Φ + w0 Fgure 4: e BF for a prescrpto 3 Expermets ad Evaluatos From expereced doctors, we gathered 40 real herbal prescrptos for hgh blood Fgure 5: A example of serous degrees of 6 fected dsease states The ferece rule gve by Eq () s equvalet to the followg fuzzy rule: IF fuzzy severtes of symptoms H THEN Dsease state s s ad ad s (9) wth certaty factor A D usg the rule form gve by Eq (9) may eed thousads of ferece rules wth may combatos of symptoms premses ur D uses ust l ferece rules by usg the rule Eq () wth Eqs (), (3) ad (4)

6 Expereced physcas have cofrmed that t was easy to revew the kowledge preseted by the rules Fgs 6 ad 7 show terfaces of the applcato for advsed prescrpto ad dagoss, respectvely 4 Coclusos We preseted a D usg the Gaussa Dstrbuto for dagoss ad BF Neural Network for prescrpto hgh blood pressure dsease M, The results cofrmed that ths D has hgh performace ad hgh applcablty for dagoss prescrpto the dsease However, f a patet has other dseases, doctors caot solely rely o ths system sce they do ot have evdece to cotrol potetal effects of the herbal gredet o the other cocurret dseases Hece, t s recommeded that the system be used oly for patets wth hgh blood pressure dsease aloe, ot for those wth other cocurret dseases ur future works are to apply GA fdg the best sets of BF parameters, to complete ths D applcato, ad the to re-evaluate the system the real patets ad compare system s results wth the doctors dagoses bserved symptoms ad ther severtes Lst of treatmet herbs wth adusted amout Fgure 6: Iterface of advsed prescrpto Clcal symptoms ad evertes slde electedsymptomsadsevertes Dstrbuto of fected dsease state Fgure 7: Iterface of dagoss efereces [] TThuy, PDNhac, HBChau: Hao Medcal Uversty - Lectures retal Medce, Medce Pub Hao, 00 [] JDurk: Expert ystem - Desg ad Developmet, Pretce Hall Ic, New York, 994 [3] MF Abbod, Dedrch G vo Keyserlgk, Derek A Lkes, MMahfouf: urvey of utlzato of fuzzy techology Medce ad Healthcare, Fuzzy ets ad ystems 0, pp , 00 [4] NH Phuog, N TThuy, CThag, D THeu: Buldg a fuzzy expert system for sydrome dfferetato the oretal tradtoal medce, Proceedgs of the Hao Iteratoal ymposum o Medcal Iformatcs ad Fuzzy Techology (MIF 99), pp , 999 [5] NHPhuog, Patprabhob, KHrota: Fuzzy Modelg for Modfyg tadard Prescrptos of retal Tradtoal Medce, J of Advaced Computatoal Itellgece ad Itellget Iformatcs, Vol7, No3, pp , 003 [6] MBelmote-errao,Cerra, Lopezde Mataras, ENI: A expert system usg fuzzy logc for rheumatology dagoss, Iterat J Itell ystems 9 () (994) [7] Dybowsk ad VGat: Clcal applcatos of Artfcal eural etworks, Cambrdge Uversty Press 00 [8] CThag, EWCooper, YHosho ad KKame: A Proposed Model of oft Computg Dagosg Dseases ad Prescrbg Herbal Prescrptos by retal Medce, Proceedgs of the Frst Iteratoal Coferece o Complex Medcal Egeerg (CME005), pp , 005 [9] CM Bshop: Neural Networks for Patter ecogto, xford Uversty Press, 005 [0] Hayk: Neural Networks, A Comprehesve Foudato, Pretce Hall, 994 [] ABezeraos, Papadmtrou, ad D Alexopoulos: adal bass fucto eural etworks for the characterzato of heart rate varablty dyamcs, Artfcal Itell Medce, 5(3), pp 5 34, 999