A Neural Network Approach in Predicting the Blood Glucose Level for Diabetic Patients

Transcription

1 Worl Acaemy of Scence, Engneerng an Technology A Neural Network Approach n Prectng the Bloo Glucose Level for Dabetc Patents Zarta Zanun, Ong Paulne an Cemal Arl Abstract Dabetes Melltus s a chronc metabolc sorer, where the mproper management of the bloo glucose level n the abetc patents wll lea to the rsk of heart attack, kney sease an renal falure. Ths paper attempts to enhance the agnostc accuracy of the avancng bloo glucose levels of the abetc patents, by combnng prncpal component analyss an wavelet neural network. The propose system makes separate bloo glucose precton n the mornng, afternoon, evenng an nght ntervals, usng ataset from one patent coverng a pero of 77 ays. Comparsons of the agnostc accuracy wth other neural network moels, whch use the same ataset are mae. The comparson results showe overall mprove accuracy, whch ncates the effectveness of ths propose system. Keywors Dabetes Melltus, prncpal component analyss, tme-seres, wavelet neural network. D I. INTRODUCTION IABETES Melltus (DM) s a chronc an progressve metabolc sorer, where accorng to the Worl Health Organzaton there are appromately 7 mllon people n ths worl sufferng from abetes. The number of abetc patents s epecte to ncrease by more than 00% by the year 030 []. Common manfestatons of abetes are characterze by nsuffcent nsuln proucton by pancreas, neffectve use of the nsuln prouce by the pancreas or hyperglycema. Causes lke obesty, hypertenson, elevate cholesterol level, hgh fat et an seentary lfestyle are the common factors that contrbute to the prevalence of abetes. Development of renal falure, blnness, kney sease an coronary artery sease are types of the severe amage whch are resulte by mproper management an late agnoss of abetes. Even though there s no establshe cure for abetes, neehe bloo glucose level of abetc patents can be controlle by well-establshe treatments, proper nutrton an regular eercse []-[3]. Treatment program for a abetc patent usually nvolves several tmes of nsuln ose necton per ay. Beses, base on the avce of a physcan, self-montorng of bloo glucose level wth a bloo glucose measurng avce s also Ths work was supporte n part by the Mnstry of Scence Technology an Innovaton (MOSTI) e-scence Grant uner Grant SF043. Zarta Zanun s wth the School of Mathematcal Scences, Unversty Scence of Malaysa, 800 Penang, Malaysa ( phone: ; fa: ; e-mal: zarta@cs.usm.my). Ong Paulne s wth s wth the School of Mathematcal Scences, Unversty Scence of Malaysa, 800 Penang, Malaysa (e-mal: ongpaulne930@yahoo.com). Cemal Arl s wth Natonal Acaemy of Avaton, AZ045, Baku, Azerbaan. one by the patent hmself. Incorporatng the nformaton lke prevous nsuln necton ose, prevous bloo glucose measurement an mealhe mofcaton to the etary, eact tme an ose of nsuln necton can be etermne by the patent, wth the a of a measurng evce. Currentlyhere are a number of fferent computer assste approaches that have been erve for the self-montorng of bloo glucose level, whch conssts of compartment moel [4], algorthmc moel [5]-[6] or mathematcal moel [7]-[8]. Howeverhe full pcture of the bloo glucose metabolsm s much more complcate. There are many etectable factors lke foo ntake an physcal eercse (Table I) an unetectable factors that characterze the bloo glucose control. The complety of the metabolsm an the uncertanty assocate wth the measurement of the factors make the mentone approaches to be less accurate an more uncertan. Hence, we seek for an alternatve precton tool whch s more relable n prectng the bloo glucose level. In ths paper, we propose an epert system whch s base on prncpal component analyss (PCA) an wavelet neural network (WNN) wth fferent embee wavelet famles n the hen layer (Mecan Hat, Gaussan wavelet an Morlet) n the precton of bloo glucose concentraton. Snce the nteractons between the factors for glucose metabolsm are comple, multmensonal, hghly nonlnear, chaotc, stochastcally an tme varant tme-sereshe neural network moel seems to be a more sutable prector, where t can moel the nput-output behavor of the glucose metabolsm, wthout knowng the nvolve eplct nternal processes. In fact, neural network moels have been apple wely n prectng tme-seres ata [9]-[]. Dfferent types of neural network moels have been use n moelng the bloo glucose metabolsm, such as the back propagaton neural network []-[7], recurrent neural TABLE I DETECTABLE FACTORS THAT INFLUENCE THE BLOOG GLUCOSE LEVEL Increase Bloo Glucose Level Foo Intake Infecton Obesty Inactvty Decrease Bloo Glucose Level Eercse Stress Insuln Low Foo Intake network [6], [8], neural fuzzy system [9]-[], Bayesan neural network [], multlayer perceptrons (MLP) [3]-[6], polynomal network [4], raal bass functon neural network (RBFNN) [3], [7], Elman neural network [8] an tme-seres convoluton neural network [6] have been 98

2 apple wely n aressng the precton of bloo glucose concentraton. Howeverhe applcaton of WNN n moelng the bloo glucose metabolsm has never been stue by other researchers. Inee, we woul see later that the nature of WNN makes t a sutable tool for the forecastng of the tme-seres for the bloo glucose metabolsm. Ths paper s organze as follows. In secton IIhe use methoology of the propose epert system an materals are scusse, followe by the smulaton result of the propose system n bloo glucose concentraton precton n secton III. Performance comparson between the propose system an the other neural network moels whch use the same ataset as ths paper s also mae n secton III. Fnally, conclusons an future work are gven n secton IV. Worl Acaemy of Scence, Engneerng an Technology Bloo Glucose Concentraton Mornng Interval Afternoon Interval II. MATERIAL AND METHODOLOGY A. Data Acquston The ata s prove by Kok [5], whch covers a contnuous pero of 77 ays from one patent. At each ay, the patent wll nee to fll n all the requre nformaton n a form, shown n Table II. As shown n Table II, a ay s splt nto eght measurement ponts: nght (NT), before breakfast (BB), after breakfast (AB), before lunch (BL), after lunch (AL), before nner (BD), after nner (AD) an before sleepng (BS). The measurement ponts are further categorze nto four fferent ntervals: mornng, afternoon, evenng an nght, where each nterval conssts of three measurement ponts, whch are, at the start of nterval, urng the nterval an at the en of the nterval (for eample: before breakfast, after breakfast an before lunch). The patent nees to fll n the nformaton for tme of glucose measurement, bloo glucose concentraton, ose of short actng nsuln necton, ose of long actng nsuln necton, foo ntake, stress an eercse at each tme of ata recorng. The scale of one to fve s use for the recorng of stress an eercse, where the value of one ncates no eercse at all an very relang, whereas the value of fve means heavy eercse an heavy stress. TABLE II SAMPLE OF THE DAILY INFORMATION FOR A PATIENT TO FILL IN 9 Aprl 004 Fray NT BB AB BL AL BD AD BS Tme 8:7 :5 7:9 3:4 Glucose Level Short Act. Insuln Long Act. Insuln Foo Eercse 3 3 Stress NT-nght, BB-before breakfast, AB-after breakfast, BL-before lunch, AL-after lunch, BD-before nner, AD-after nner, BS-before sleep, Act-Actng Snce the unts for these varables are not the same, hence the ata are scale nto mean of 0 an stanar evaton of before the precton of glucose concentraton s one by the neural network Day Fg.. Bloo glucose concentraton of a patent for a pero of 77 ays for the mornng an afternoon nterval. The unt for the glucose concentraton s mmol/l. The connectng lnes between the measurement ponts o not contrbute any value An eample of a tme-seres for the bloo glucose concentraton for the mornng an afternoon ntervals s shown n Fg.. It can be observe that the tme-seres for the bloo glucose concentraton s hghly non-statonaryme varant an chaotc. Hence, a neural network moel appears to be a sutable tool to capture the behavor between the nput-output mensons. The correlaton coeffcents, R, between the bloo glucose concentraton of mornng, afternoon, evenng an nght ntervals, calculate by usng formula () are shown n Table III, cov(, y) R, R () st()st(y) where cov(, y) s the covarance between varable an y, st() an st(y) are the stanar evaton of varable an varable y respectvely. From the statstcal analyss of correlaton coeffcent, R ncates an ncreasng lnear relatonshp, whereas R ncates a ecreasng lnear relatonshp between varable an y. The values between the range R measure the egree of lnear epenence between the varables. The varables are sa to be hghly correlate to each other f R 0.5. From Table III, obvouslyhe bloo glucose concentratons for the mornng, afternoon, evenng an nght ntervals are not hghly correlate to each other. For eample, a correlaton coeffcent of 0.68 s obtane between the mornng an afternoon ntervals. Ths observaton s probably ue to the fact that the factors whch control the glucose metabolsm are omnatng at fferent peros. The factors are omnatng for a few hours, but are not contnuous for the pero that s longer than t. For eamplehe short actng nsuln s necte before each meal n orer to accommoate for the ncrease bloo glucose concentraton after the ntake of foo, but the effect of ths short actng nsuln can only stan for 3 to 4 hours. Thus s uncorrelate over a ay pero. 98

3 Worl Acaemy of Scence, Engneerng an Technology TABLE III CORRELATION COEFFICIENTS BETWEEN THE BLOOD GLUCOSE CONCENTRATION FOR THE MORNING,AFTERNOON,EVENING AND NIGHT INTERVALS Mornng Afternoon Evenng Nght Mornng Afternoon Evenng Nght Therefore, base on the fact that the values for these four ntervals are uncorrelate, separatng the bloo glucose precton nto four fferent neural network moels s reasonable, where one neural network moel s bult for each nterval. B. Feature Selecton It has been proven that ncluson of the past measurement of the nput varables ncreases the precton accuracy of the neural network moel conserably [8]. However, glucose metabolsm process changes wth tme. Thushe moelng of glucose metabolsm whch nvolves numbers of nternal/eternal factors as well as past hstory of the factors tself s cumbersome. Ths s where the PCA plays ts role, where t s capable of etractng the characterstc features of ths complcate process. Assume that along the tme-seres atahere are recurrent characterstc features wth a scale of L. Movng a wnow of length L along the tme-seres wth a step s at a tme s comparable to fnng the vectors of the corresponng menson. Hencehose recurrent characterstc features wll keep repeatng n the wnow vector. Ths wnow vectors are the proecte onto bass vectors through PCA. By applyng PCAhe orgnal wnow vectors whch reflect the temporal varablty of the bloo glucose concentratons (fragment of tme-seres ata) are transforme nto a lower mensonalty of nepenent orthogonal prncpal components (PC). The PC wll only correspon to the basc component of the characterstc features, where ths mples that there s only one sgnfcant PC for those recurrent features, even though t repeats for more than once n the tme-seres. Hence, a hghly rregular tme-seres ata wll be transforme nto a new regular tme-seres of PC scores, whch enables an easer precton. A bref eplanaton of PCA technque s gven below [8]. Let X m n be the ata matr wth n column vectors an m elements n each vector. Frstly, a covarance matr, C, of the ata matr s calculate by usng eqn. () C N T ( )( ) () N where s the mean of X. Subsequentlyhe egenvalues,,..., N an the corresponng egenvectors of the covarance matr E, E,..., E N are calculate. Thenhe egenvalues are sorte n ecreasng orer. Hence, one can create the kth PC, y k by usng the eqn. (3) y k T E k X, k,,..., N (3) In facthe PC s the lnear combnaton of orgnal ata. The frst PC represents the recton of mamum varaton n the ata, where the secon PC, whch s orthogonal to the frst PC, represents the net largest varaton n the ata an so on. Snce most of the varaton n the ata s concentrate n the frst few PCs, hence choosng the frst few PCs are accountable for most of the varablty. The number of PC to choose can be etermne by usng eqn. (4) 3... w p 00% (4) 3... N where w N. A total of w PCs are chosen when the value of p ecees a certan percentage. In orer to make the performance comparsonhe same 9 nput varables as Kok [5] an Bagha an Nasraba [7] are use, whch are shown n Table IV. By applyng PCAhe mensonalty of 9 varables s reuce to 4 PCs for the mornng, afternoon, evenng an nght ntervals respectvely, snce the frst 4 PC alreay omnate for more than 90% varablty. Subsequentlyhs frst 4 PCs wll be the nput for the WNN moel, whch wll be scusse n the net secton, an the output of the WNN wll be the precte bloo glucose concentraton at the en of each nterval. C. Bloo Glucose Level Precton Base on Wavelet Neural Network There are a number of fferent neural network moels whch ffer n the network archtecture, learnng algorthm, number of hen layers, an also the type of actvaton functon use n the hen layers. WNN s one of the neural network moels whch s nspre by the smlartes between the wavelet ecomposton an the sngle hen layer neural network. Snce the frst mplementaton by Zhang an Benvenste [9]-[30], WNN have been apple wely n TABLE IV THE INPUT VARIABLES FOR THE PRECTION OF BLOOD GLUCOSE LEVEL. Glucose Level Durng Interval. Short Actng Insuln Durng Interval 3. Foo Intake Durng Interval 4. Eercse Durng Interval 5. Glucose Level At Start of Interval 6. Long Actng Insuln Durng Past 4 Hours 7. Stress Durng Interval 8. Glucose Level At Start of Interval on Prevous Day 9. Short Actng Insuln Durng Interval on Prevous Day 0. Foo Intake Durng Interval on Prevous Day. Eercse Durng Interval on Prevous Day. Resultng Glucose At En of Interval on Prevous Day 3. Glucose Level At Start of Prevous Interval 4. Short Actng Insuln Durng Prevous Interval 5. Foo Intake Durng Prevous Interval 6. Eercse Durng Prevous Interval 7. Eercse Average Over Past 4 Hours 8. Interval Length Durng Interval 9. Eercse Ae Up Square Values Durng Past 4 Hours 983

4 varous applcatons, such as system entfcaton, classfcaton, an pattern recognton problems [3]-[33]. WNN ffer from the other neural network moels n that t nvolves the ntegraton of wavelet famles as the actvaton functon n the hen noes. A schematc agram of a WNN, wth nput noes, m hen noes an L output noes s shown n Fg.. The nput layer wll receve the nput varable (,..., ) an transmt the accepte nput varables to the net layer. The secon layer s a hen layer wth a mother wavelet n each hen noe. In ths paper, fferent wavelet famles, namely Mecan Hat, Gaussan wavelet an Morlet are selecte as the bass functons n hen layer. The noes n ths layer are gven by the prouct of the mother wavelet as ( ) ( D (X t ) ),,..., m (5) where D an t are the scalng an translaton vector respectvely. The sgmo functon (logstc an hyperbolc tangent) s the commonly use bass functon n an MLP. Compare to the bass functons n the hen layers of the MLPhe mother wavelet use n the hen noes of a WNN s a localze actvaton functon. Hencehe connecton weght assocate wth the hen noes can be vewe as locally pecewse constant moels, whch leas to learnng effcency an structure transparency. y y... y L Output w w m Layer Fg.. A schematc agram of a wavelet neural network wth nput noes, m hen noes an L output noes The thr layer s the output layer. The output wll be the lnear combnaton of the weghte sum of the hen layer, whch s gven by eqn. (6) y m k () w ( D (X t ), k,,..., L (6) where w an are the weght vector an bas term between hen layer an output layer respectvely. Obvously, all the neurons n any layer are fully connecte to the preceng an also the succeeng layer, but no connectons between the neurons wthn the same layer are allowe. Varous learnng algorthms can be apple to the tranng of WNN; n ths paperhe learnng of WNN s by the metho of solvng the pseuo-nverse wth fe parameter ntalzaton. Therefore, only the weght matr W nees to be auste urng the tranng of WNN, n orer to map the unerlyng relatonshp between nput an output space. Before we begn to escrbe the learnng algorthm of WNN, let us efne the cost functon as n eqn. (7) Worl Acaemy of Scence, Engneerng an Technology Hen Layer Input Layer E(f(n)) (y (n) y(n)) (7) where y s the esre output value an y(n) s the output value from WNN. Hencehe tranng of WNN s base on the mnmzaton of the cost functon. There are two stages nvolve. Frstlyhe scalng parameter s fe. Nethe translaton vector s ranomly chosen from the nput vectors. Let us represent eqn. (6) as Y W, where (, D ) (, D )... (, D m m ) (,D ) (, D )... (,D m m ) (8) : : : : (, D ) (,D )... (, D m m ) s the output of wavelet famles, an (, D ) ( D ( t ) ). Therefore, n orer to solve the weght matr W, W Y s compute. s the pseuo-nverse efne as T ( ) T. A summary of the learnng algorthm of WNN s gven as below:. Intalze the values for D an t.. Fee n the nput vector X nto the WNN.. Calculate the prouct of the hen layer by usng eqn. (5). v. Solve for the weght matr W by usng the pseuo-nverse metho. v. Obtan the output value of WNN, y (n) from step (v). v. Compare y (n) wth the esre output value, y. v. Calculate the cost functon as n eqn. (7). v. Repeat steps () to (v) untl t meets the stoppng crteron. D. Performance Ine The tranng of the WNN moel nvolves the mofcaton of the weght vector graually, n orer to mnmze the fference between the precte value an the esre response. The fference s referre as cost functon, whch can be measure by varous crtera. In ths paperhe performance ne n terms of root mean square error (RMSE) s use, whch s formulate as p RMSE [f( ) y ] p (9) where p s number of testng samples, f( ) s the esre response an y s the precte output of WNN. E. Multfol Cross Valaton 984

5 Ecessve tranng wll force the WNN to memorze the nput vectors an nsuffcent tranng wll cause the WNN to be unable to learn from the nput vectors presente to t, where t wll lea to poor generalzaton when new nputs are presente to the WNN. Therefore, n orer to avo these problems, multfol cross valaton s use. The samples are ve nto k groups, where k. Frstly, one group from the samples s left out, where the tranng of the neural network nvolves the remanng of the samples. Nethe valaton error s measure by testng t on the group left out. The process s repeate for k tmes, usng each fferent group respectvely as the testng set. Subsequentlyhe average of the valaton error s calculate. In ths stuy, we use a 0-fol cross valaton. III. EXPERIMENTAL RESULTS As scusse n Secton II, four fferent WNN moels wll be constructe separately for the mornng, afternoon, evenng an nght ntervals, n orer to prect the bloo glucose concentraton at the en of each nterval. There are two stages nvolve n the precton of the bloo glucose concentraton by usng the propose system. Frstlyhe nput menson whch conssts of 9 nput varables s reuce to 4 PCs by usng the PCA technque. Subsequentlyhe PCs wll be the nput to the nput layer of the WNN. After proper trannghe output from the WNN wll be the precte bloo glucose concentraton at the en of the nterval. The epermental results (n terms of RMSE) of ths propose system by usng fferent wavelet famles n the hen layer of WNN are shown n Table V. Worl Acaemy of Scence, Engneerng an Technology change n ts man feature wll manage to etect the great changes n the tme-seres of bloo glucose concentraton. TABLE V PREDICTION OF BLOOD GLUCOSE LEVEL USING THE PROPOSED SYSTEM WITH DIFFERENT WAVELET FAMILIES IN THE HIDDEN LAYER OF WNN Wavelet RMSE for Dfferent Intervals Famles Mornng Afternoon Evenng Nght Mecan Hat Gaussan Wavelet Morlet Bloo Glucose Concentraton Actual Concentraton Precte Concentraton Day Fg. 3. A plot of tme-seres for the precte an actual measure bloo glucose concentraton at the en of the mornng nterval by usng the propose system wth Gaussan wavelet n the hen layer of the WNN A. Performance Assessment an Dscusson From Table V can be seen that all the propose system wth fferent wavelet famles n the hen noes performe well n prectng the bloo glucose concentraton at en of each nterval. The hghest RMSE s at for the mornng nterval an the lowest RMSE s at for the nght nterval. These RMSEs are rather small, ncatng a promsng performance of the propose system. A plot of the tme-seres for the precte an measure bloo glucose concentratons at the en of mornng nterval by usng the propose epert system wth Gaussan wavelet n hen layer of WNN s shown n Fg. 3, where Fg.4 s the enlarge segment of Fg. 3, for the frst 30 ays. It can be seen that, a relatvely goo precton can be obtane by usng the propose epert system. From the epermental resultshe propose epert system wth Gaussan wavelet n the hen noes of WNN prouces the lowest RMSE for each nterval, when t was compare wth the WNN wth Mecan Hat or Morlet n the hen layer. As shown n Fg. he tme-seres for the bloo glucose concentraton s hghly rregular. It vares conserably an t has a number of bg leaps throughout the tme-seres. From the wavelet analyss, hgh ntensty sgnal s prouce when the ata correlates strongly to the shape of the wavelet. Thus, usng the wavelet famles whch have the shape that s entcal to the shape of the features beng nvestgate s crucal. In other worshe wavelet famles that have a great y Bloo Glucose Concentraton Day Actual Concentraton Precte Concentraton Fg. 4. An enlarge segment of the tme-seres n Fg. 3, for the frst 30 ays Fg. 5a. A schematc agram for Mecan Hat 985

6 Worl Acaemy of Scence, Engneerng an Technology y y Fg. 5b. A schematc agram for Gaussan wavelet Fg. 5c. A schematc agram for Morlet The schematc agram of the wavelet famles whch we use n ths stuy, namely Mecan Hat, Gaussan wavelet an Morlet, s gven n Fg. 5. These wavelet famles are symmetrc (Mecan Hat an Morlet) or ant-symmetrc (Gaussan wavelet) an they are hghly regular. Wavelet famles of Mecan Hat an Morlet work well n appromatng the quas-snusoal functon [34]. However, from Fg. he tme-seres s of a saw-tooth shape, nstea of snusoal. Thereforehe wavelet famles that have a large change n ts shape wll be more sutable n prectng the saw-tooth tme-seres, where Gaussan wavelet satsfes ths crteron. The man feature of ths ant-symmetrc Gaussan wavelet mght eplan the goo precton accuracy for the saw-tooth tme seres that are use n ths stuy. The present glucose metabolsm s affecte by the past glucose metabolsm. That s the reason why the nput varables from the prevous nterval are also consere n moelng the glucose metabolsm n a partcular nterval (Table IV). Howeverhe bloo glucose concentraton at a gven nstant, even though epenng on the past hstory of the nput varables, shoul only be lmte wthn a certan short tme nterval. When the pero of tme between two consecutve measurements of the bloo glucose level are gettng longerhe nfluence of the hstory of the varables from the prevous nterval are not sgnfcantly mportant anymore. Somehowhs hstory nformaton mght become useless an a nose to the ata. The length of the tme nterval between two consecutve measurements for the nght nterval an the mornng nterval of the followng ay spans a pero of aroun 8 hours, whch s a long pero of tme. Ths mght eplan the lowest accuracy of the bloo glucose precton for the mornng nterval. B. Performance Comparson an Dscusson Performance comparson between the propose epert system wth the other neural network moels, namely, MLP [5] an RBFNN [7] are mae, usng the same ataset n earler stues. The same 9 nput varables (Table IV) are consere, but fferent nput selecton approaches are apple. Kok [5] use MLP n prectng the bloo glucose levels, where a heurstc approach s use n selectng the nput varables for the tranng of the neural network moel. By tral an error, Kok came out wth fferent combnatons of the nput varables, an the MLP were trane by these fferent nput combnatons for each mornng, afternoon, evenng an nght nterval. Performance crtera are calculate from the testng samples, where the partcular nput selecton metho that performe the best for each nterval was selecte, followe by the bloo glucose levels precton by usng an MLP. As a result from ths heurstc approach, Kok use nput varables (, 3, 4, 5, 6, 7, 8, 9) for the mornng an nght nterval, (, 3, 4, 5, 7) for the afternoon nterval an (, 5, 3, 4, 3, 4, 5, 6) for the evenng nterval (Please refer Table IV for the nformaton on nput varables). Followe from Kok, Bagha an Nasraba [7] use an RBFNN n moelng the bloo glucose metabolsm, where a prunng metho was apple n the nput selecton. Frstly, the RBFNN was trane by all the 9 nput varables. After the tranng of RBFNNhe crteron functon as well as the weght of each nput varable was calculate. The nput varable wth a low magntue of weght was elmnate, followe wth the tranng of RBFNN agan. The crtera functons were measure agan. Elmnaton of the nput varable s worthy, f the present crteron functons were better than the prevous one. Otherwsehe elmnate nput varable wll not be omtte. The process was repeate untl the optmal nput varables for each nterval were obtane. In the en, after the elmnaton of the varables, nput selecton of (,, 5, 6, 7, 0, 5, 7, 8) for mornng nterval, an (,, 5, 0, 5, 8) for afternoon, evenng an nght nterval are obtane (Please refer Table IV for the nformaton of nput varables). The performance comparson between these fferent nput selecton methos an prector base on fferent neural network moels s gven n Table VI. The performance of the propose epert system wth Gaussan wavelet n the hen layer of WNN s use for the comparson. From Table VI, a combnaton of nput selecton by heurstc approach an prector base on MLP use by Kok performe unsatsfactorly. Ths sappontng performance s probably ue to the poor nput selecton an also the behavor of the MLP. Selectng the combnaton of nput varables ranomly mght overlook the mplct relatonshp between the varables snce there are stll many unetectable relatonshps between the factors that affect the bloo glucose metabolsm. Hence, selectng the nput varables by tral an error seems to be not a wse approach. Beses thathe sgmo functon use as the actvaton functons n the MLP s a globalze actvaton functon. Compare wth the localze wavelet functons n the hen layer of WNN whch leas to learnng effcencyhe learnng of the MLP s tme consumng. In atonhe back propagaton learnng algorthm use by MLP tens to get trappe n local mnma. When the MLP s unable to converge to a global mnmumhe precton accuracy of the MLP wll eterorate. Thushs mght eplan the poor performance of Kok s result. 986

7 From Table VIhe performance of the propose system outperforms the RBFNN use by Bagha an Nasraba [7] overall. Regarless of the nput selecton approach, when we only emphasze on the prectve capablty of RBFNN an WNNhe latter seems to be a more approprate prector for the tme-seres ata. Ths s manly ue to the actvaton functon use n the hen layer of RBFNN an WNN. In [4]he Gaussan functon s use n the hen noes of RBFNN. However, a peroc functon an an eponental functon are appromate better by usng a WNN wth an oscllatng wavelet bass functon an by a Gaussan actvaton functon respectvely. When we look at the nature of the tme-seres of the bloo glucose concentraton, appromatng ts saw-tooth behavor s more sute by usng WNN. Ths mght ustfy the superor performance of the propose system for the mornng, afternoon an evenng ntervals, when ts prectng accuraces are compare aganst wth the performance of RBFNN. For the nght nterval, a RMSE of 0.08 s obtane for the RBFNN, whereas for the propose system, a RMSE of s acheve. In ths ntervalhe performance of RBFNN s more superor. Ths mght be cause by the nput selecton metho. When we look at the selecte nput varables for the nght nterval use by the RBFNN approach, the number of factors n the prevous nterval, prevous ay or the past 4 hours are elmnate. Compare wth the PCA technque that we use for the nput selectonhe nfluence for all the factors that play an mportant role n bloo glucose metabolsm, as well as the hstory for all the factors are taken nto account n the PCA. However, even though the bloo glucose concentraton at a gven nstant epens on ts past hstory, when the length of the tme nterval s too longhe past hstory of the nput varables on the prevous ay or past 4 hours mght not nfluence the present bloo glucose concentraton anymore. We shoul clarfy that when we menton about the term past 4 hours ncates the tme nterval start from 00:00am to 4:00pm on the prevous ay. Hencehe length of the tme nterval between the measurement ponts for the nput varables urng the past 4 hours s longer for the nght nterval, compare wth the mornng, afternoon an evenng ntervals. Hence, when we take t nto the conseraton by usng the PCA mght eterorate the prectve capablty of the WNN. IV. CONCLUSION Elevaton of bloo glucose level abruptly wll make the abetc patents go nto a coma. Hence, precton of the bloo glucose concentraton s mportant, n orer for the patents to aust the ose for nsuln necton an for preventng the severe complcatons that result from mproper management of the bloo glucose levels. TABLE VI PERFORMANCE COMPARISON BETWEEN THE PROPOSED SYSTEM AND THE OTHER NEURAL NETWORK MODELS RMSE for Dfferent Intervals Mornng Afternoon Evenng Nght Kok [8] Bagha an Nasraba [4] The Propose System Worl Acaemy of Scence, Engneerng an Technology In ths paper, we have propose an epert system, where a feature selecton base on the PCA an a prector base on WNN are use. Our epermental results showe that the propose epert system s a powerful moel for the bloo glucose precton an t outperforme both the MLP an RBFNN approach. The better performance of the propose system mght be attrbute to the ntegraton of wavelet famles n the hen layer of WNN, whch can be eplane by the fact that wavelet functons can capture the behavor of the chaotc tme-seres better, compare wth the sgmo functon n an MLP an the Gaussan functon n a RBFNN. The promsng epermental results are also probably contrbute by the nput selecton by usng PCA, where the nfluences of the nput varables at present as well as ts past hstory are taken nto conseraton of the bloo glucose metabolsm. Howeverhere s an ssue relate to the tme constant. Some of the past hstory of the nput varables that we have consere n the PCA, span for more than 36 hours from the present measurement pont. The queston that arses here s, how far behn from the current measurement pont shoul we take nto account. In future work, beses the ssue of the tme constant for the past hstory of the nput varableshe other sgnfcant factors that nfluence the glucose metabolsm, lke weght, age an se, as well as the physologcal parameters such as heart rate an skn mpeance can be ntegrate n the moelng of the neural network. Development of a system that nvolves the precton of the bloo glucose level, as well as recommenaton for the approprate therapy s favorable. 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