The Application Model of BP Neural Network for Health Big Data Shi-xin HUANG 1, Ya-ling LUO 2, *, Xue-qing ZHOU 3 and Tian-yao CHEN 4

Transcription

1 2016 Internatonal Conference on Artfcal Intellgence and Computer Scence (AICS 2016) ISBN: The Applcaton Model of BP Neural Network for Health Bg Data Sh-xn HUANG 1, Ya-lng LUO 2, *, Xue-qng ZHOU 3 and Tan-yao CHEN 4 College of Medcal Informatcs, Chongqng Medcal Unversty, Chongqng, Chna, E-mal: @163.com glo@163.com *Correspondng author Keywords: BP neural network, Applcaton model, Health bg data. Abstract. Neural network s one of the tools n processng ntellgent nformaton, whch s wdely used n computers and other varous dscplnes. The artcle reveals the bg data n health fled and the characterstcs of t, t ntroduces the basc prncple of BP neural network and structure of the wdely used network, ntroduces the desgn step, comples the applcaton n varous areas of health and applcaton strategy of BP neural network, summarzes the weakness of neural network applcatons n the health feld and puts forward the optmzaton method. Introducton Wth the rapd development of IT technology such as cloud computng, nternet of thngs, moble Internet, the data of medcal and health ndustry rse exponentally. medcal bg data formed bascally, and brought many changes to our lves, the health care ndustry has gradually formed a large data structure that speak n data, usng data decson-makng, usng data management and data nnovaton. among them, how to mne and dscover valuable medcal nformaton from the medcal and health bg data has become a top prorty. Medcal and health ndustry bg data has the characterstcs of 4V:(1) Volume: huge volume. the volume and varety of medcal and health ndustry, such as: n 2014 only the outpatent data storage of a hosptal n Chongqng account for space of up to more than 300 GB. (2) Varety: medcal and health ndustry has dfferent types of data structure, such as, the laboratory test data (LIS) presented a structured data type, the electronc medcal record data (EMR) presents the sem structure data type and medcal mage data (PACS) presents an unstructured data type and so on. (3) Velocty: health data s real-tme, hgh speed data flow, whch s often dffcult to be fled n a fxed locaton. Nowadays, the promoton of moble medcal equpment and health data transmsson requre a hgh speed, effectve way. (4) Value: core features of medcal bg data: there are many nterference, synergy and data qualty defects n the feld of health care. the proporton of the value of the data s small, the greatest value of bg data s to dg out the value of the future trend and pattern predcton analyss through the data from a large number of dfferent types of data, and through the depth analyss of data mnng methods, fndng new rules and new knowledge, and appled to the feld of health care, to mprove medcal and health, promote the health scences study on the effect. Compared wth tradtonal data analyss methods, BP neural network has obvous advantages, manly n the: compared to other data mnng methods, BP neural network model has a hgh degree of nonlnear mappng ablty(processng of unstructured data, ncludng text, mages, vdeo, audo and other data), hgh fault tolerance(bp neural network uses the whole approach, some sample errors wll not affect the overall structure), hghly adaptve and self-learnng ablty(bp neural network can approxmate the tranng sample by changng the weght of the model). therefore, snce the end of the 1980s, BP neural network has been wdely concerned by researchers from all walks of lfe, and has been wdely used n varous felds of scentfc research. n the feld of health care, BP neural network n the dagnoss of dsease, to explore the rsk factors of dsease, predct the ncdence of dsease, to explore the economc burden of the patent has been a lot of applcatons. 281

2 obvously, the applcaton of BP neural network to tap the vast amount of data n the feld of health care, has become one of the hot ssues n the current medcal communty. Under the background, ths paper attempts to explore and study the applcaton mode of BP neural network whch s opposte to the large data of medcal and health care. The Basc Prncple of BP Neural Network The Connotaton of BP Neural Network Artfcal neural network (artfcal neural networks, ANN) s one of the ntellgent nformaton processng tools, t s a collecton of electronc, computer, bology, mathematcs and physcs knowledge, to solve the current computer or other system stll cannot solve the problem by some organsms n the neural network structure smulaton and functon. Accordng to the network topology, the artfcal neural network can be dvded nto four types: forward to the network, there s feedback to the former network, the layer before the nterconnecton network and the whole or part of the Internet. Among them, error back propagaton neural network model s a multlayer feedforward neural network, ths model s one of the wdely used neural network models [1]. as a result of the use of BP algorthm named. the back-propagaton algorthm solves the hdden layer neurons contanng multlayer network lnk weght learnng. The Structure, Desgn and Operaton Mode of BP Neural network The Structure of BP Neural Network: A three-layer feedforward neural network structure based on BP algorthm s shown n Fgure 1. t s composed of nput layer, hdden layer and output layer and requred to determne the number of nodes n the nput layer and output layer, select the number of layers and nodes n hdden layers. Fgure 1. The structure of feedforward neural network. In Fgure 1, the x s the nput layer, the O j s the hdden layer, the Ok s the output layer, the W and Wkj are connected value. Incentve functon s usually used n s-shaped functon or Gaussan functon, whose expresson are varance, s the gan value. The desgn of BP neural network: 1 f( x) and ax 1 e 2 x f( x) e V, x s the mean,v s the The confrmaton of tranng sample. Tranng samples are orgnate from the random unbased samples. t's sze should be approprate, f the sze of t s excessve wll cause the speed of tran slowly, the external realty poorly, the generalzaton ablty lowly, f t s too small to make the result relable and representatve. the number of sample sze and connected value s 10 to 1,whch can acheve the requrements[2]. The normalzed sample. The nput and output varables of the sample were normalzed to the sample values normalzed to[ 1,1].t makes the network tranng more effcent, mprove the speed of tranng and the performance. t could take the followng ways: 282

3 x x /( x x ) (1) b,max,mn x x /( x ) b,max b x s the normalzed varable of the j nput of the sample, x,max and x,mn are the maxmum and the mnmum of the j nput of the sample. The ntalzaton of BP neural network. To ntalze the weghts of the network, the selecton of ntal weght s not approprate, t wll take too much tme to tran and the error trap n local mnmum. The random number between the ntal weghts [ 1,1]. The desgn of nput layer. The number of nput neurons s consstent wth the number of nput varables. The desgn of hdden layer. (1)The number of hdden layer: Three layer BP network can map arbtrary n-dmensonal to m-dmenson. any contnuous functon n the closed nterval can be approxmated by a sngle hdden layer network, t takes less tranng tme. (2)The number of hdden unts: The determnaton of the number of hdden unts s complex, there s no deal method to solve t. lots of attempts to determne the optmal number n general. Deng W[3] use the nformaton entropy to estmate the optmal number of hdden unts, the followng formulas are usually used to determne the extent of the hdden unts. n 0 C n1 k n, f 1, C n 0 n1 n m a n1 log n 2 n s number of nput unts, k s sample sze, n s the number of hdden unts n the layer, The value of a s constant between[1,10]. The desgn of output layer. The output layer s one layer, the number of neurons n the output layer s accordng to the expected number of predcted varables m or log m 2. Operaton mode of BP neural network The operaton mode of BP neural network s dvded nto the forward propagaton and the error back propagaton[4], correctng weghts to acheve the desred effect. Learnng perod In the learnng perod, the state of computng unt s constant and the weghts of the connectons can be modfed by learnng samples. BP neural network defnes ths perod as: the sgnal s transmtted, t s transferred to the hdden layer and calculated the actual output of each node. Performance perod The connecton weghts are fxed and the state of calculaton unt s changed to acheve the stable state. BP neural network defnes ths perod as: the error back propagaton; The error between the actual value and the expected value s calculated by the recursve calculaton and the weght s modfed by the error. For a group of samples (N for sample sze),the system error s: 1 M E x ( n) d( n) 2N n 2 Usng gradent descent learnng method, the weght of BP algorthm s adjusted by the followng formula: (2) (3) (4) (5) (6) 283

4 ...E W ( t 1) W ( t) W m W ( t 1) s the weght matrx of the ( t 1) teraton, W () t s the weght matrx of the t teraton, E E s error functon, W range of s 0 1. m s weght correcton, t s the number of teratons, s the step sze, the Applcaton strategy of BP neural network Ths paper takes dabetes as an example, construct BP neural network applcaton model n medcal large data. (1)Establsh tranng samples. Frst, the goal of BP neural network applcaton n medcal data mnng s determned--independent rsk factors for early death n patents wth dabetes. Secondly, extracton and ntegraton of samples n the electronc medcal record database, and the electronc medcal record data warehouse s formed. Fnally, n order to determne the rsk factors for premature death n patents wth dabetes. collected from 2013 to 2000 electronc medcal records n the database of data samples, screenng out the requred 22 knds of sample propertes. (2)Normalzaton of sample processng. the example uses the formula 3. (3)BP network ntalzaton. the ntal weght of ths case s a random number between [-1,1]. (4)Desgn nput layer. the nput layer unt s each sample object(x1,x2,x3,,x118765),set the attrbutes of the patent, ncludng: Medcal record NO, dsease number, name, age, brth place, psychology, respraton, lver cancer, colon cancer, bladder cancer, breast cancer and so on(p1,p2,p3,,p22). (5)Desgn hdden layer. the hdden layer s selected as one layer, and the three layer BP network s constructed. the number of unts s determned by a number of attempts to fnd the optmal number, each unt s ultmately set as a patent's physologcal propertes. (6)Desgn output layer. the output layer s vascular dsease, cancer, nfectous dseases, external causes, ntentonal self njury, degeneratve dsease(o1,,o6). After the BP network model s bult, tranng samples of normal populaton. after many tmes of tranng, mean square varance s changed to 0, then seek out the ncdence of some dseases P, so as to get the threshold of the correspondng dsease P total sample sze of the expermen, set threshold k 0. Fnal output Ok value. n whch O1=38526,O2=35847,O3=16318,O4=7530,O5=452 8,O6=4136. Results show: In the rsk factors of early death n patents wth dabetes, the probablty of vascular dsease and a varety of cancer (ncludng lung cancer, colon cancer, lver cancer, pancreatc cancer, bladder cancer and other cancers) s relatvely large, followed by nfectous dseases, external causes, ther own ntentonal njury, degeneratve dsease and so on..(7) Input layer Hdden layer Output layer.o8o6 Fgure 2. the neural network structure of the rsk factors of death n dabetes. 284

5 The Applcaton of BP Neural Network n Health Feld Dagnose Dseases Zhang PJ use 9 genes to dagnose prmary lver cancer[5].103 cases that prmary hepatocellular carcnoma who get chronc hepatts wth B nfecton and 54 cases of control group, t use the Logstc regresson analyss, dscrmnant analyss, classfcaton tree and artfcal neural network to establsh the mult parameter model for gene dagnoss and evaluate ts dagnostc value. The area whch s under the curve, senstvty and specfcty of the dagnostc model were 0.943, 98%and 85%. The proposed model provdes an auxlary dagnostc method. Detect Rsk factors of Dsease and Complcatons Ma XM collected 233 cases of samples were analyzed[6], t compared multple factors logstc regresson model wth the model of BP neural network n mean value of rsk factors of the hand-foot-and-mouth dsease. BPNN model can reflect the complcated nonlnear relatonshp between the rsk factors of HFMD and tself. the fttng effect and the correct rate of classfcaton n network are better and BPNN can get nteracton factor of Logstc regresson model. the thermal peak s more than 39 C,the sprt of poor and leukocyte ncreased have nteracton n BPNN model. Predct the Incdence of Dsease Xu XQ collected the measles annual ncdence rate n [7]. The ncdence rate of was tranng sample and the ncdence rate of was the test sample. tranng model based on BP neural network algorthm, forecastng the ncdence data of measles n the smulaton model structure shows the average relatve predcton error of sample s 1.908%, the predcton error of examnaton s 2.332%, the neural network has better predcton accuracy n the ncdence of measles Explore the Influencng Factors of the Fnancal Burden of Patents Explore the Influencng Factors of Hosptalzaton Expenses Wang GL collected the medcal records of cerebral nfarcton n Tangshan Cty n , the number s 2459[8]. The BP neural network model was establshed to analyze senstvty n the nfluence factors and the nfluence factors on the hosptalzaton expenses were measured by the senstvty analyss. the study used Newton algorthm, the ndex of R 2 and Rc 2 are larger,the fttng ablty and generalzaton ablty are better. the nfluence factors of the model have the actual economc value. Predct the Number of Days n Hosptal Wu G collected the medcal records of ntracranal hemorrhage n the 309th Hosptal of the Chnese people's Lberaton Army n ,the number s 396[9].BP neural network model was establshed to predct the number of days of hosptalzaton n patents wth ntracranal hemorrhage, used R 2 and Rc 2 as evaluaton ndcators to compare wth multple lnear regresson model. the results of the two comparson s that BP neural network model has the advantage (t=4.099, P<0.01), the average relatve error s smaller. Conclusons Through the studes, the followng conclusons are obtaned: 1. Ths paper descrbes the basc prncple of BP neural network. and dscusses the applcaton strategy of BP neural network n the feld of health care, n ths paper, the applcaton of the rsk factors n the dagnoss of dsease, dsease and complcatons were revewed, and the nfluence factors of the dsease ncdence rate and the economc burden of the patents were predcted. 2. In the BP neural network model, there are stll some lmtatons. (1)BP neural network hdden layer model of uncertan unt number, are often based on experence repeated test selecton. (2) Hypothess test of weght coeffcent, confdence nterval weght coeffcent, sgnfcant weght 285

6 coeffcent, these ssues reman to be resolved.(3) BP neural network model error surface gradent s more complex, n the error curve, there wll be a strong shock and a long plane, so that the number of tranng greatly ncreased, thus affectng the convergence rate. at the same tme, the BP neural network model system error s easy to converge to the local mnmum ponts of the network error. 3. In vew of the defects of BP neural network, n addton to further enhance the performance characterstcs of the BP neural network, can be nonlnear processng methods, combned wth the new method of sgnal processng and neural network, to form a more optmal performance n the network, such as wavelet analyss, shows good performance[10]. Marquardt- Levenberg numercal optmzaton algorthm (L-M algorthm) s a knd of fast convergence speed, sutable for small and medum-szed network of the mproved BP neural network algorthm. we can also use the genetc algorthm to search for the characterstcs of the search technology to reduce, and then the characterstcs of the reducton as the BP neural network nput varables, tranng and constructon of BP neural network model, because of the unque workng prncple of genetc algorthm, t can search the global optmzaton n complex space, especally sutable for optmzaton based on complex neural network model, ths knd of neural network combned wth genetc algorthm s one of the most effectve ways. the use of BP neural network combned wth other data mnng methods (such as: ant-colony algorthm, L-M algorthm) to be appled n medcal and health feld s more mature, provde more valuable nformaton for healthcare workers. Acknowledgement Ths research was fnancally supported by grant 15BGL191 of Natonal Socal Scence Foundaton n Chna. Reference [1] Ye J.W., Shen Y.C., Huang X.L. Analyss of hosptalzaton expenses of medcal nsurance based on BP neural network. Health Economcs Research, 2013, 26(6): (In Chnese) [2] Martn S., Werner V., Renhard R. Neural networks and logstc regresson: Part I. Comp Stat Data Analy, 1996, 21(): 661. [3] Deng W. Research on the constructon and optmzaton of BP neural network and ts applcaton n medcal statstcs. Fudan Unversty. Doctoral Thess. 2003, 12, 23. (In Chnese) [4] Zhou C.G., Lang Y.C. Computatonal Intellgence. Jln Provnce: Jln Unversty Press, 2001: (In Chnese) [5] Zhang P.J., Tan Y.P. Evaluaton of the dagnostc value of perpheral blood mult parameter genes n the dagnoss of prmary hepatocellular carcnoma. Labeled Immunoassays and Clncal Medcne, 2014, 21(5): (In Chnese) [6] Ma X.M., Su M.L., Duan G.C. The rsk factors of hand-foot and mouth dseases n BP neural network model of predcton analyss. Chna Publc Health, 2014, 30(6): (In Chnese) [7] Xu X.Q., Du J.L., Sun N. Applcaton of mproved BP neural network model n measles predcton. Chna Journal of Modern Medcne, 2014, 24(31): (In Chnese) [8] Wang G.L., Wu J.H., Y S.F. Analyss of nfluencng factors of hosptalzaton expenses of patents wth cerebral nfarcton based on BP neural network. Modern Preventve Medcne, 2010, 37(23): (In Chnese) [9] Wu G., Xu G.Y., Ba Y. Applcaton of BP neural network and multple lnear regresson model to predct the number of days of hosptalzaton n patents wth ntracranal hemorrhage. Journal of Shanx Medcal Unversty, 2015, 46(10): (In Chnese) [10] Wang E.M., Lu T.F., Wang X. Wavelet neural network model for mage and pattern recognton. Journal of Chna Unversty of Mnng, 2002, 31(5): (In Chnese) 286