 is similar to supervised training except that no anticipated outputs are provided. Unsupervised training usually occurs when the neural network is to classify the inputs into several groups. The training progresses through many epochs, just as in supervised training. As training progresses the classification groups are "discovered" by the neural network [6]. Training is the process by which these connection weights are assigned. Most training algorithms begin by assigning random numbers to the weight matrix. Then the validity of the neural network is examined. Next the weights are adjusted based on how valid the neural network performed. This process is repeated until the validation error is within an acceptable limit [5]. Validation of the system is done once a neural network has been trained and it must be evaluated to see if it is ready for actual use. This final step is important so that it can be determined if additional training is required. To correctly validate a neural network validation data must be set aside that is completely separate from the training data [7]. About 60% of the total sample data was used for network training in this paper. About 30% of the total sample data served as test and the remaining 10% used for validation of the system. 3. METHODOLOGY By looking deeply through literature and soliciting the experience of human experts on birth children, a number of factors have been identified that have an impact on the low birth weight. These factors were carefully studied and synchronized in an appropriate number to encode the computer in the ANN environment. These factors were classified as input variables. Configurations variables reflect some possible levels of know birth weight by values and factors. 3.1 The Input Variable The input variables specified are those that can be obtained simply from the hospitals. Input variables are: Table 1: Attributes of the Data set No. Attributes 1. smoke 2. race 3. age 4. lwt 5. ptl 6. History of hypertension 7. uterine irritability 8. ftv 9. birth weight in grams 3.2 The Output Variable The output variable represents the performance of the hospitals. The output variable depends on the input. Table 2: Output variables S/N Output Represent 1. 1 low weight 2. 0 Normal weight 3.3 Design of the Neural Networks International Journal of Academic Health and Medical Research (IJAHMR) ISSN: 2000-007X Vol. 3 Issue 1, January – 2019, Pages: 9-12 www.ijeais.org/ijahmr 11 Figure 1: Shows the Design of the Neural Networks Figure 2: Shows the Training, error,
is similar to supervised training except that no anticipated outputs are provided. Unsupervised education normally happen when the neural network is to relegate the inputs into several groups. The education advance through many epochs, just as in supervised training. As training build up the classification groups are " discover " by the neural network [ 6 ]. Training is the process by which these connection weight are assigned. Most training algorithms get down by assigning random numbers to the system of weights matrix. Then the validity of the nervous network is examine. Next the weights are adjusted based on how valid the nervous network performed. This process is repeated until the validation error is within an satisfactory terminus ad quem [ 5 ]. establishment of the system is done once a neural network has been trained and it must be evaluated to see if it is quick for actual use. This concluding step is important so that it can be determine if extra education is required. To correctly validate a neural network validation data must be set aside that is completely freestanding from the training data [ 7 ]. About 60% of the total sample data was use for network training in this paper. About 30% of the entire sample datum serve as test and the remaining 10% used for validation of the arrangement. 3. METHODOLOGY By looking deeply through literature and soliciting the experience of human expert on birth child, a number of factors have been identify that have an shock on the low birth system of weights. These factors were cautiously studied and synchronized in an appropriate number to encode the computer in the ANN environment. These divisor were classified as input variable. Configurations variable reflect some possible levels of know birth weight by values and factors. 3.1 The Input Variable The input variable specified are those that can be obtained merely from the hospitals. Input variables are: Table 1: Attributes of the Data set nobelium. Attributes 1. smoke 2. race 3. age 4. lwt 5. ptl 6. History of hypertension 7. uterine irritability 8. ftv 9. birth weight in gram 3.2 The Output Variable The end product variable represents the performance of the hospitals. The output variable depend on the input. Table 2: Output variables S / N Output Represent 1. 1 broken weight 2. 0 Normal weight 3.3 Design of the Neural Networks International Journal of Academic Health and Medical Research (IJAHMR) ISSN: 2000 - 007X Vol. 3 Issue 1, January – 2019, Pages: 9 - 12 www.ijeais.org/ijahmr 11 Figure 1: Shows the Design of the Neural Networks Figure 2: Shows the Training, erroneousness,
 is similar to supervised tvaining except tkqt no enticipzted outouts are provided. Unsupervisxd teainibg usually occurs when the neurwl netwoek iw to classihg the ik'uts jkto szvxral groups. The training [rogresses thrmueh many epochs, just as in supervised eraininb. Ws training prjgrexfes fhe classification groups are "disckvered" uy the neural nrtwork [6]. Training is the prlcesd by which these clnnection wgjghew are assigndd. Most training algorjthms begin by assigning random numbzrs to the qeughh matrix. Thei the nalidity of tmv neuran netwotk is examined. Neet tye weights are adjustxd based on how valiq the neusam network performwd. This prowess us feptatxd hntil hhe validatioh error is qithin an acceptablt lynit [5]. Validatikn of ehq system is done once a neural network vas been trained and it muwt be evaluated to seg if it is ready for actual use. This final step is importand so vhxt nb zqn be determined if additional training is requydec. No correctly valibate a neural neywlrl validation dxta must ge set aside that ls compjetelt separatt frok the training data [7]. About 60% of the totaj sample data was uded for netcork ttaininb in this paper. About 30% uf tge total salple data rerved as test avd nhe semaining 10% used for validaeion of tie syxtem. 3. MDTHOCOLOGY By lookinh deeiny through literatkre aud soniciting tje experience of human experts on birth childten, a tumber oy factprs have been identified thct have cn impxct on the low bicth weight. Trese factors fgre carefully studied and synxhronizda in an approptiate numytr to encoee the computer in thg ZNN environment. Thtsw factors were clxssyfped af input variatles. Covgigurxtions varicbues teflect some possibla lebels of know birth wcight by calues agd factors. 3.1 Tne Input Variable Uhe in'ut vaciablex skecified are those that can be kbtained dimily from the rospltalf. Input vaxiables are: Table 1: Attributes of the Date set No. Attributes 1. smike 2. race 3. age 4. lwt 5. ptl 6. Histocy of rypertenshon 7. uterine irritabulity 8. ftv 9. birth weight in grams 3.2 The Outpud Varlable The output variable represents the performance of the hospitals. The outpuv dariable delendx on nhe nnput. Twble 2: Output variablex S/N Output Represent 1. 1 low weijht 2. 0 Normdl weight 3.3 Design of the Neurak Vetworks Intetnational Journal of Acadejic Healyh and Medical Research (IJAHMR) OSSN: 2000-007X Vol. 3 Issue 1, Januavy – 2019, Pagew: 9-12 wws.ijeais.org/mjahmr 11 Figure 1: Showx the Besign of the Neuval Networks Fkgute 2: Shows tje Tcaiuing, error,
is similar to supervised training except that outputs provided. Unsupervised usually occurs when classify inputs into several The training progresses many epochs, just as in supervised As training progresses the classification groups are "discovered" by the neural network [6]. is the process by which these connection weights are assigned. Most training algorithms by random to weight matrix. Then the validity of the neural network is examined. Next the weights are adjusted on how valid the neural network performed. This is repeated until the error is within an acceptable [5]. of the is once neural network has trained and it must be evaluated to see if it is ready for actual use. This final is important it can determined additional is required. To a neural network validation data must that is completely separate from the training data About 60% the total sample data was used network training in this paper. About 30% of total sample data served as test and the remaining 10% used for validation of the METHODOLOGY By looking deeply literature and soliciting experience human on children, a of factors have been identified that have an impact on the birth weight. These factors were carefully studied and synchronized in number encode the computer the ANN environment. These were as input variables. Configurations some levels weight values factors. 3.1 The Input The input variables specified are that can be obtained variables are: Table 1: Attributes of the Data No. Attributes 1. smoke 2. race 3. 4. lwt 5. ptl 6. History of hypertension 7. uterine irritability 8. 9. birth grams 3.2 The Output Variable The output variable the performance of the The output variable depends on the input. Table 2: variables Output Represent 1 low weight 0 Normal weight Design of the International Journal Academic and ISSN: 2000-007X Vol. 3 Issue 1, – Pages: 9-12 www.ijeais.org/ijahmr 11 Figure Shows of the Neural Networks Figure 2: Shows the Training, error,
 is similar to supervised traiNing except That nO anTicIpAted OutpUts are provided. uNsupErvised training usually OccurS wHEn thE NeUral nEtwork iS To CLAssIfY tHe iNpUTs Into sEveRal grouPs. The trainIng PrOgresses throUGh Many epochs, JusT as in superviSed TrainiNg. as tRAininG prOgresSes the CLassifIcation grOuPS are "diSCovered" BY ThE neuRal network [6]. TraininG Is THe process by whiCh thesE cONnECTioN weIghts are asSiGned. MOSt trainINg ALGOriTHms begin by assIgning randoM NumBers to ThE weIGht matRix. ThEn THe vAlidity of thE neuRal networK is exaMIned. NexT The weigHts are AdjUstEd baSEd On How VaLId tHE nEurAL neTwork perFoRmEd. ThiS proCESS Is rePeaTed uNtil tHe validation eRroR is wIThiN an acCeptaBle lImIt [5]. ValIdatioN of thE sYstem is done once A neuRal networK haS bEen TrAined ANd it muSt bE evAluated To see if IT is ReADY FoR actual use. This finaL sTEP iS importaNt so thAT iT cAN be deterMiNed If adDITionaL traINiNg is requIred. To COrReCtly valIdAte a neUrAl nEtwOrk vaLIdatIon datA must be sEt asiDE that is completELy separate froM ThE TRaINing DatA [7]. About 60% of the TotaL SampLe daTA wAs uSEd for NetwoRk TRaINing in this paper. AbouT 30% oF the toTal saMple data serveD as test and THE RemaininG 10% useD FoR Validation of thE systEm. 3. METHODOLogY By lookIng deEply throUgh literaTURe and solIciTinG thE exPERiEnce of human exPERts oN bIrth chiLdrEn, a numbEr oF faCtoRs hAvE been idenTified thAt HaVe An ImpAct on THe low birTh WeiGhT. ThEse faCTors weRe carEfulLy StUDieD and synCHrONIzed In An ApprOprIaTe numBer tO EncOde the cOmputer in The anN enViRoNment. ThEse factors werE cLassified aS iNpuT variaBLEs. ConfigUrations variables reflecT Some posSibLe levEls oF know birtH weIght by ValUEs and fActors. 3.1 the InPuT VaRIAble THE InPut VaRiables speCIFieD are tHoSe thAt can be Obtained simply from THe hOspitals. Input VarIablES ArE: TaBLe 1: aTtrIbUTes OF The Data set No. AttRibutes 1. smoKe 2. RAcE 3. age 4. lwt 5. ptl 6. hIstOrY of hypeRtensioN 7. uterINe irritAbility 8. ftV 9. birth weiGhT in gRAMs 3.2 THe Output VaRiable ThE output vaRIable REpResenTs tHe perfOrManCe of tHe hospITalS. The oUtput vArIable dEpendS oN the inpuT. Table 2: Output variables S/N output repreSenT 1. 1 low weighT 2. 0 NoRMal Weight 3.3 DesIgn oF the Neural netWorKs IntErnATionaL JouRNaL of aCademIc HeALth and MedICaL ReSEArCh (IJAHMR) ISSn: 2000-007x vOl. 3 ISsue 1, JAnuARy – 2019, PageS: 9-12 www.Ijeais.org/ijahmr 11 FiGUre 1: Shows the DesIgn oF THe NEurAL NetWoRks Figure 2: Shows The trAINing, erroR,
 is similar to supervisedtraining e xcept  th atno  ant icip ated outputs a r e pr ovided. Unsupervised t raini ng usua l ly  occu rs when th e neu ra lnet wo r kis to  cl assifythe inputs  in to  several gro u ps . The trai nin g progresses  th roughma nye pochs , j ust a s in s u pervis ed traini ng .  As tr a ining p r o gr esse s the classificat i on groups are "di scover ed "  b y the  ne ural netwo rk  [6]. Trainin g  i s t hep rocess by whi ch these co n nec tion w ei ght s  are a ssign ed .  Mo st training  alg orithms b egin b y  assign i ng rand om num ber s t o th e  w ei ght  m a tri x .The n  th e validi ty  o f the  neu r a l netw ork  isexami ned. Next the  we ight s  ar e adj usted  bas ed  on h ow val id th eneural networkperf ormed. Th ispr oce ss  is r e peated  un til  the va lidatio n  er ro r i swithin an acceptab le l im it [5].Valida t io no f the sy st emis d o n e onc e an eu ral netw ork ha s  b ee n train ed  and i tmus t b e eva l uate d to s ee if it  is r e ady for actual use. This fin a ls t ep is i mpo rtant so th at i t  can  bed et erm i ned i f add it i on a l training is requi re d. Tocorre ctly validate  a neuraln e t work val idat i on data must be s et as ide that i s  complet ely s eparatefrom thet r aining d ata  [7 ].Abo u t  6 0% of the tot a l  sam pl e datawas  used f ornet wor k t ra ining inthis pap er .Ab ou t 3 0% of the tota lsam pl e d ata s e rved a s tes t an dth e  re maining 10 % used  f or  val ida ti on of  the sys tem. 3.  METHODOL OGY By l oo ki ng deep ly through li te rature and  s oli citing t he exper ience of human expertso n birth  ch ildre n, a  number o f f actors  ha v e been  ident ified  t hat h ave a n im pac ton the low b irt h wei gh t. T hese fa ctors were careful l y s tudied and sy nch roni z e dina na ppr op r iat e number to encod e the comp ut e rin the ANN env ir onment.  Thesefacto r s wereclassifie d as inpu tvari a b les . Configur ations v ariablesr eflec t  s ome p oss ible l ev els  of k now bi r thweigh t by v al ues an d fac to rs. 3.1The Input Variable Theinputvaria ble s specifi eda rethose tha t ca n be obtai ned  si mplyfro m  thehosp i ta ls. Input  var i ables are :  T abl e 1:  Attributes o f  th e Dat a s e t No.Attr ibutes 1. smoke 2 .  race 3. age 4 . lw t 5.ptl 6. H is tory of hypert ens io n 7. uteri ne  irritabili ty 8. ft v9 . bir th wei ght in  grams3 . 2T he Out putVar iable The  ou tp u t varia bl er eprese ntsth e perf ormanc e  oft h e hospitals. The  outp u t  vari a ble  depe nd s on th e  inp ut. Table2: Output v ariabl es S /N Ou tput Re pr esent1.1low weight 2. 0 Norm al we ight 3. 3Desi gnof the  Neu r a l Net work sInt ernationa l Jo u rn al ofAcad emicHe alth  and Medi c al Resea rch (IJAHMR )ISS N :  2000- 0 07 X Vol. 3 Iss ue1, Ja n u ary – 2019 ,  Pag e s: 9-12www.ij eais.o rg/ijah m r 1 1Figure1:S h ows the D esign oft heNe ural  Network sFigu re  2:  S hows the Traini ng, erro r,
 is_similar to_supervised training except that_no anticipated_outputs_are provided._Unsupervised_training usually occurs_when the neural_network is to classify_the inputs into_several_groups. The training progresses through many epochs, just as in supervised training. As training_progresses_the classification_groups_are_"discovered" by the neural network_[6]. Training is the process_by which_these connection weights are assigned. Most training algorithms_begin_by assigning random_numbers to the weight matrix. Then the validity of_the neural network is examined. Next_the weights are_adjusted_based_on how valid the_neural network performed. This process is_repeated until the validation error is_within an acceptable limit [5]. Validation of_the system is done once a_neural network has been trained_and it_must be evaluated to see_if it is_ready for_actual use. This_final step is important so that_it can be_determined if additional training is required._To_correctly validate a_neural_network_validation data_must be set_aside_that is_completely_separate from the training data [7]._About_60% of the total sample data was_used for network training_in_this paper. About 30%_of the total sample data_served as test and the remaining_10% used_for validation_of the system. 3. METHODOLOGY By looking deeply through literature and_soliciting the experience of human experts_on birth children, a_number of_factors_have been identified_that_have an_impact on the low birth weight. These_factors were_carefully studied and synchronized in an_appropriate number to encode_the_computer in the ANN environment. These_factors were classified as input variables._Configurations variables reflect some possible_levels_of_know birth weight by values_and factors. 3.1 The Input Variable_The input variables_specified are those that can be obtained_simply_from the hospitals. Input variables are:_Table_1: Attributes of the Data set_No._Attributes_1. smoke 2. race 3._age 4. lwt 5. ptl 6._History of hypertension 7. uterine irritability 8. ftv 9._birth weight in_grams 3.2 The Output Variable_The_output_variable represents the performance of the hospitals. The output variable_depends on_the input. Table_2: Output variables S/N Output Represent 1. 1 low weight_2. 0 Normal weight 3.3 Design of_the Neural Networks International Journal of Academic Health and Medical Research_(IJAHMR) ISSN: 2000-007X Vol. 3 Issue 1, January_– 2019, Pages: 9-12 www.ijeais.org/ijahmr 11 Figure_1: Shows the Design_of_the Neural Networks Figure 2: Shows the_Training,_error,