Document 2 bjil 05042017

ISSN (ONLINE): 2454-9762 ISSN (PRINT): 2454-9762 Available online at www.ijarmate.com International Journal of Advanced Research in Management, Architecture, Technology and Engineering (IJARMATE) Vol. 3, Special Issue 10, March 2017

PERFORMANCE OF K-NEAREST NEIGHBOR BASED CLASSIFIER FOR CLASSIFICATION OF CARDIOTOCOGRAMS JagannathanD1 1

M.Phil. (PGScholar)Dept of Computer Science ,Dr.C.V.RamanUniversity, Chhattisgarh.

Abstract:Fetalheartrate(FHR)anduterinecontractions(U C)aresimultaneouslyrecordedbyCardiotocography(CT G).TheCTG,whichisoneofthemostcommondiagnosticte chniquesusedtoevaluatematernalandetalwellbeingduringpregnancyandbeforedelivery.Byobservingt heCardiotocographytracepatternsdoctorscanunderstan dthestateofthefetus.Thereareseveralsignalprocessingan dcomputerprogrammingbasedtechniquesforinterpretin ga typicalCardiotocographydata.Evenfewdecadesafterthei ntroductionofcardiotocographyintoclinicalpractice,the predictivecapacityofthesemethodsremainscontroversial andstillinaccurate.InthisresearchworkIproposeanintegr atedmethodologyforCTGanalysisandclassification.Ano velsetoffeatures,derivedfromthetimeandfrequencydom ains,is usedto feedthenewtoolforpatternclassification,namedKNearestneighbor.WeusedAccuracy,Specificity,NPV,Pr ecision,RecallandROCasthemetrictoevaluatetheperfor mance.Thearrivedresultsprovethat,eventhoughthetradit ionalclusteringmethodscanidentifytheNormalCTGpatt erns,theywereincapableofSuspiciousandPathologicpatt erns.Itwasfoundthat,theKNearestneighborbasedclassifierwascapableofidentifyin gNormal,SuspiciousandPathologiccondition,fromthen atureof CTGdata.

Keywords— CTG,Datamining,Classification,fetalheartrate,uterine contractionsandK-Nearestneighbor

beconsideredadditionalknowledgeaboutthedata.Thekn owledgeobtainedthroughtheprocessmaybecomeadditio naldatathatcanbeusedforfurthermanipulationanddiscov ery.Applicationofdataminingconceptstothemedicalaren ahasundeniablymaderemarkablestridesinthesphereofm edicalresearchandclinicalpracticesavingtime,moneyan dlife.Clinicaldataminingistheapplicationofdataminingt echniquesusingclinicaldata.ClinicalDataMining(CDM)involvestheconceptualization,extraction ,analysis,andinterpretationof availableclinicaldataforpracticalknowledgebuilding,clinicaldecisionmakingandpractitionerreflection.Themainobjectiveofcl inicaldataminingistohaulnewandpreviouslyunknowncli nicalsolutionsandpatternstoaidthecliniciansindiagnosis, prognosisandtherapy.Moreoverapplicationofsoftwares olutionstostore patient recordsinanelectronic formisexpectedtomakeminingknowledgefromclinicald ata lessstressful.

A.Cardiotocography(CTG) Sincethe1960’s,obstetriciansareusingtheCardi otocography,anelectronic methodforrecording(graphy)thefetalheartbeat(cardio)a nduterinecontractions(toco)duringpregnancy,bymeans ofaCardiotocographoranelectronicfetalmonitor(EFM).

Fig.1illustratesatypicalCardiotocogram(CTG).

I. INTRODUCTION Dataminingreferstoacollection oftechniquesthatprovidethenecessaryactionstoretrievea ndgatherknowledgefromanexhaustivecollectionofdataa ndfacts.Dataisavailableinenormousmagnitude,butthekn owledgethatcanbeinferredfromthe dataisstillnegligible.Dataminingconceptsarefocusedon discoveringknowledge,predictingtrendsanderadicating superfluousdata.Discoveringknowledge inmedicalsystemsandhealthcarescenariosisaherculeany etcriticaltask.Knowledgediscoverydescribestheprocess ofautomaticallysearchinglargevolumesofdataforpattern sthatcan

Figure1.AtypicalCTG[4]

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Thecontinuousmonitoringbyusing CTGrequiresqualitativeandquantitativeinterpretationso fseveral parametersdescribedasfollows[3]: Uterine activity(contractions): • Frequency:Numberofcontractioninastandard interval.

• Duration:Theamountoftimefromthestartofaco ntractiontotheendofthesamecontraction.

• Intensity:Ameasureofhowstrongacontractioni s.

• Restingtone:Ameasureofhowrelaxedtheuteru sis betweencontractions.

• Interval:Theamountoftimebetweentheendofon econtractiontothebeginningofthenextcontracti on.

Uterineactivitymaybe definedas: • Normallessthanorequalto5contractionsin10minutes,a veragedover a30-minutewindow.

• Tachysystolemorethan5contractionsin10minutes,averaged overa30-minutewindow.

Baselinefetalheartrate(FHR),whichisdeterminedb yapproximatingthemeanFHRroundedtoincrementsoffi vebeatsperminuteduringa10minutewindow,excludingaccelerationsanddeceleration sandperiodsof marked FHRvariability.

•

•

Baseline FHRlessthan110beatsperminuteandsymptoms aretermedasBradycardia.

BaselineFHRgreaterthan160beatsperminutea ndsymptomsaretermedastachycardia.

BaselineFHRvariability,whichisdeterminedina10minutewindow,excludingaccelerationsanddeceleration s.BaselineFHRvariabilityisdefinedasfluctuationsintheb aselineFHRthatareirregularinamplitudeandfrequency.T hefluctuationsarevisuallyquantifiedastheamplitudeofth epeak-to-troughinbpm(beatperminute).

• • • •

Absent Minimal Moderate Marked

Presenceofaccelerations:Visuallyapparentabruptin creaseinFHR.Anabruptincreaseisanincreasefromanons etofaccelerationtothepeakinlessthanorequalto30second s(tobeconsideredasacceleration, the peakmustbe greaterthanorequal to15bpm).

Periodic orepisodic decelerations • Periodic:Referstodecelerationsthatareass ociatedwithcontractions.

Episodic:Referstothosenotassociatedwithcont ractions Therearefourtypesofdecelerations: • Earlydeceleration:Itisrelatedtoagradualdecrea seintheFHRwithanonsetofdecelerationtoanadi r(morethan30seconds)wherethenadiroccurswi ththepeakof acontraction.

• Latedeceleration:Itisrelatedtoagradualdecreas eintheFHRwithanonsetofdecelerationtoanadir (morethan30seconds).

• Variabledeceleration:Itisrelatedtoanabruptdec reaseintheFHR(morethan15bpm)thatwasmea suredfromthemostrecentlybaselinewherefrom thedeceleration’sonsettonadirislessthan30sec ondsandthedecelerationlasts(morethan15seco nds).

• Prolongeddeceleration:Itispresentwhenthereis avisuallyapparentdecreaseinFHRfromthebase linethatisgreaterthanorequalto15bpm,lastingg reaterthanorequalto2minutes,butlessthan10mi nutes.Adecelerationthatlastsgreaterthanorequ al to10minutesisa baseline change.Changesortrendsof FHRpatterns overtime.

• CategoryI(Normal):Baselinerate110160bpm,Moderatevariability,Absenceoflate,o rvariabledecelerations,andearlydecelerationsa ndaccelerationsmayormaynotbepresent.

• CategoryII(Indeterminate):Tracingisnotpredi ctiveofabnormalfetalacidbasestatus,butevaluationandcontinuedsurveill anceandrevaluationsareindicated.

• CategoryIII(Abnormal):Absenceofbaselineva riabilitywithrecurrentlateorvariabledecelerati onsorBradycardia;orsinusoidal fetal heartrate •

II. RELATEDWORK TheInternationalFederationofObstetricsandG ynaecology(FIGO)guidelines[5]wereintroducedasan attempttostandardizetheuseofelectronicmonitoringofF HR.ThefirstworkofautomaticCTGanalysisfollowingFI GOguidelinesconsistsindescribingandextractingtheCT Gmorphologicalfeatures[6].Bernades[7]developedSisP orto,asystemforautomaticanalysisof CTGtracings,basedonanimprovementofthemorphologi cal featureextractionintroducedin[6].

ArtificialNeuralNetworks(ANNs)wereusedas aclassifier[8]todetectFHRaccelerationanddecelerationp atternsandtoestimatethe FHRbaselineandvariability.ANNswereusedtoclassifyd ecelerationpatternsintoepisodicandperiodicdeceleratio ns[9]accordingtoFIGOguidelinesandbasedontherelatio nshipbetweentheparametersof

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thedecelerationandtheassociateduterinecontraction.AN NswithRadialBasisFunctions(RBF)andMultiLayerPerc eptrons(MLP)werethebestperformingclassifiers.

SupportVectorMachines(SVM)havebeenused forFHRsignalanalysis.Georgoulasetal.[10]useddiscrete wavelettransformationtoextractscaledependentfeaturesoftheFHRsignalandSVMfortheirclas sification.Georgoulasetal.[11]usedSVMwithRBFand polynomial kernelstoidentifyfetal andneonatalcompromise,namelymetabolicacidosis[12] .TheRBFkernelmachinesoutperformedthepolynomial machinesandbothofthemoutperformedtheconventional methodsofk-nearestneighbor(kNN),linearandquadraticdiscriminantclassifiers.

Chudáčeketal.[13]usedSVM,naïveBayes,and adecisiontree(C4.5algorithm)withapolynomialkernelto analyzeFHRsignalsbasedonlinearfeatures(e.g.Descripti onoftheFHRbaselineusingmean)andnonlinearfeatures( e.g.Fractaldimensionofwaveform).TheyusedthreeFSm ethods:PrincipalComponentAnalysis,InformationGain, andGroupofAdaptiveModelsEvolution(GAME).

Krupaetal.[14]proposedanewmethodforFHRs ignal analysisbasedonEmpiricalModeDecomposition(EMD)f orfeatureextractionandSVMwithRBFforclassificationo f FHRrecordings.

Georgoulasetal.[15]proposedaFSmethodbase donbPSO(binaryParticleSwarmOptimization)forFHR signalanalysisusingSVMandk-NN. Hybridmethodshavebeenalsoconsideredforaut omatedFHRsignalanalysis.FontenlaRomeroetal.[16]proposedseveralapproachesforthereco gnitionofaccelerationanddecelerationpatternsinFHRsig nals,includingrule-basedapproach,ANNs,andaneurofuzzyapproach.

III. MOTIVATIONANDJUSTIFICATIONCar diotocography(CTG),consistingoffetalheart rate(FHR)andtocographic(TOCO)measurements,isuse dtoevaluatefetalwellbeingduringthedelivery.Since1970,manyresearchersha veworkeddifferentminingmethodsto helpthedoctorsthatinterprettheCTGtracepatternfromthe fieldofsignalprocessingandcomputerprogramming[18].

WiththehelpofCTGtracepatternanalysisthedoctorswithi nterpretationsinordertoreachasatisfactorylevelofreliabil ity.So,theyactasadecisionsupportsysteminobstetrics.Fo reverydaypractice,noneofthemhasbeenadaptedworldwi de.Baselineestimationincomputeranalysisofcardiotoco graphs,whichiscurrentlynoconsensusonthebestmethod ology.Morethan30yearsaftertheintroductionofantepartu mcardiotocographyintoclinicalpractice,thepredictiveca pacityof themethodremainscontroversial.Inareviewoflotofarticl espublishedonthissubject,itwasfoundthatitsreportedsen sitivityvariesbetween2and100%,anditsspecificitybetwe en37and100%[19].So,inthis

work,wearegoingtoevaluateKNearestneighboralgorithmsforclusteringCTGdata.

IV. MATERIALANDMETHODOLOGY A.DATASETDESCRIPTION TheCardiotocographydatasetusedinthisstudyi spubliclyavailableatTheDataMiningRepositoryofUniv ersityofCaliforniaIrvine(UCI).Byusing21givenattribut esdatacanbeclassifiedaccordingtoFHRpatternclassorfet alstateclasscode.Inthisstudy,fetalstateclasscodeisuseda stargetattributeinsteadofFHRpatternclasscodeandeachs ampleisclassified intooneofthreegroupsNormal,SuspiciousorPathologic.

Thedatasetincludesatotalof2126samplesofwhichis1655 normal,295suspiciousand176pathologicsampleswhichi ndicatethe existing of fetaldistress.

Attributeinformationisgivenas:LB— FHRbaseline (beatsperminute)AC—#of accelerationspersecondFM—#of fetal movementspersecondUC—#of uterinecontractionspersecondDL—#of light decelerationspersecondDS—#of severe decelerationspersecond DP—#of prolongueddecelerationspersecondASTV— percentage of timewithabnormalshort termvariability MSTV—meanvalue of short term variabilityALTV— percentage of time withabnormal longtermvariability MLTV—meanvalue of longterm variabilityWidth—widthof FHRhistogramMin—minimumof FHRhistogramMax—Maximum of FHRhistogramNmax—#of histogrampeaks Nzeros—#of histogram zerosMode— histogram modeMean—histogram meanMedian— histogrammedianVariance—histogram varianceTendency—histogram tendencyCLASS— FHRpatternclasscode(1to10) NSP—fetalstate classcode(N=normal; S=suspect;P=pathologic) B. CLASSIFICATION Classificationprocessmaybeappliedindifferentareasofre searchandpractice,e.g.,farms,military,medicine,Earthre motesensing.Theclassicalclassificationtechniquesusest atisticalapproach,whichtypicallyassumesthenormal multidimensionaldistributionofprobabilityintheexperi mentaldataset.Dataclassificationmaybesupervisedand unsupervised.

Thesupervisedclassificationmethodrequiresthepresenc eoftrainingdatasettypicallydefinedbytheexperttheteacher.Eachclassofobjectsischaracterisedby thebasicstatisticalparameters

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(meanvaluesvector,covariancematrix),whicharevaluesv ector,covariancematrix),whicharecomputedfromthetrai ningset.Theseparametersguidethediscriminationproces s.TheBayesianclassifiersaretypicalrepresentatives(Bay esclassifier,Fisher,Waldsequential).

Theunsupervisedclassificationisalsoknownasclassificat ionwithouttheteacher.Thisclassificationuses,inmostcas es,themethodsofclusteranalysis.Thedevicethat performsthefunctionofclassificationiscalledclassifier.T heclassifieristhesystemcontainingseveralinputsthataret ransportedwithsignalscarryinginformationabouttheobje cts.Thesystemgeneratesinformationaboutthecompetenc eofobjects intoaparticularclassontheoutput.

toaheartratethatexceedsthenormalrangeforarestinghear trate(heartrateinaninactiveorsleepingindividual).Depen dingonthespeedandtypeofrhythm,itcanbe dangerous.

C. THEMEDICALBACKGROUNDOFCARDIOTO COGRAPHY(CTG)

Type 2(late)

Cardiotocographyisamedicaltestconducteddu ringpregnancythatrecordsfetalheartrate(FHR)anduterin e contractions.Eitherinternalorexternalmethodsthetestsm aybeconducted.Duringtheinternaltesting,theuterusplac edbyacatheterafteraspecificamountofdilationhastakenp lace.Theexternaltests,apairofsensorynodesareaffixedtot hemother'sstomach.TheCTGtracegenerallyshowstwoli nes.Thefetalheartrateisrecordedbytheupperlineinbeats perminuteandtheuterinecontractionsarerecordingbythe lowerlinefromtheTOCO. BaselineHeart Rate Thebaselineheartratehelpstoevaluatethehealth yfunctioningofthe cardiovascularsystem.Thebaselinefetalheartrateisdeter minedbyapproximatingthemeanFHRroundedtoincrem entsof5beatsperminute(bpm)duringa10minutewindow,excludingaccelerationsanddeceleration sandperiodsofmarkedFHRvariability(greaterthan 25bpm.Abnormalbaselineistermedbradycardiaandtach ycardia.Thefluctuationsarevisuallyquantitiesastheampl itudeofthepeak-totroughinbpm.Usingthisdefinition,thebaselineFHRvaria bilityiscategorizedbythequantitiesamplitudeas: Absent-undetectable Minimalgreaterthanundetectable,butlessthanorequalto 5bpm Moderate-6bpm-25bpmMarkedgreaterthan25bpm

Type 1(early) Thisoccursduringthepeakoftheuterinecontract ion.TheFHRwithonsetearlyinthecontractionandreturnt obaselineattheendofthecontractionwillbeuniform,repeti tiveandperiodicslowing.Thereasonsbehindthismaybefe talheadcompression,cordcompressionorearlyhypoxia.T hisoccursinfirstandsecondstagelaborwithdecentofthehe ad[21].Thisissynchronouswithuterine contraction.

Thisoccursafterthepeakoftheuterinecontractio n.TheFHRwithonsetmidtoendofthecontractionandnadir morethan20secondsafterthepeakofthecontractionanden dingafterthecontractionwillalsobeuniform,repetitivean dslowing.Ifthelagtimeishighseriousnessisalsohigh.This isalsosynchronouswithuterinecontraction.Mx:afetalpH measurementismandatory[21].

Type 3(variable) Thisisvariable,repetitive,andperiodicslowing ofFHRwithrapidonsetandrecovery.Variableandisolated timerelationshipswithcontractioncyclesmayoccur.Dece lerationpatternsintimingandshaperesemblesothertypesi nsomecases.Iftheyoccurconsistently,thereisachanceoff etalhypoxia.Thisisunrelatedtouterinecontractions.Mx:c heckfetalpHifthepatternpersistsafterturningthepatiento nherside(or ifotheradverse featuresare present)[21].

A. D.K-Nearest neighbor Thek-nearestneighborsareanexampleofaclassificationmethodwith theindependenceoftheparameters.Themethodcanbeclas sifiedasbythepointofimplicationassimple,butefficientin manydatasets[11].The kNNalgorithmisdefinedbythreeterms(S,k,T) whereSrepresentsaresemblancemeasurewhichlinkstoea chpairofdatainanappropriateNdimensionalspaceat(realorinteger)number,krepresentst henumberofnearestdatathataretrainedtocarryouttheclas sificationandTrepresentsthevectorofMtrainingdataappl iedbytheclassifiertoactuallycarryouttheclassification[1 2].k-NNusesdistancemetricsusuallyEuclideandistance toperformthe similaritymeasure formulatedby

Bradycardia: Itistherestingheartrateofunder60beatsperminu te,thoughitisseldomsymptomaticuntiltheratedropsbelo w50beats/min.Itmaycausecardiacarrestinsome patientsTachycardia:Ittypically refers

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•

• Iftisadatasample,whichclassificationimplementeda nditsknearest neighborsturnup,thenthismakesaneighborhoodoft.Whil eusingthemethodtoclassifythedatasampleintheneighbo rhoodaboutt,thedistanceiswhetherconsideredornot.Ofc ourse,tousek-NNchoosingtherightvaluefor kiscritical;duetotherateofsuccessofclassificationdirectl yappertaintothisvalue.ThekNNmethodcanpassforbiasedbyk.Itispossibletochooseth ekvalueinsomanydifferentways.Ofcourse,thesmoothande fficientoneisapplying thealgorithmseveraltimeschanging thevalueofktogetthehighestaccuracy.Todecreasethedep endencylevelofchoice ofkforkNNmethod,Wang[13]recommendedcheckingmultiples etsofnearestneighborsinsteadofonesetofnearestneighbo rs.

• • •

•

Thefalsenegativerate(FN)is theproportionofpositivescasesthatwereincorre ctlyclassifiedasnegative Theaccuracy(AC)istheproportionofthetotalnu mberofpredictionsthatwerecorrect.

TheSensitivityorRecalltheproportionofactualp ositivecaseswhicharecorrectlyidentified.

TheSpecificitytheproportionofactualnegativec aseswhichare correctlyidentified.

ThePositivePredictive ValueorPrecisiontheproportionof positivecasesthatwerecorrectlyidentified.

TheNegativePredictiveValuetheproportionofn egativecasesthatwerecorrectlyidentified.

TABLE1:PERFORMANCEANALYSISOFKNEARESTNEIGHBORUSINGCROSSVALIDATIO N

Performance

KNearestN

V. EXPERIMENTATIONRESULT

Accuracy

97.4230

A. PERFORMANCE EVALUATION Thisisameasurementtooltocalculatetheperfor mance  TP+ TN  Accuracy=   TP+ TN+ FP+ FN

Sensitivity

96.3320

Specificity

96.6219

PPV

94.7793

NPV

97.5064

ROC

91.9874

Sensitivity=



Specificity=

TP  TP+ FN

 

TN     TN+ FP 

  TP+ FP

PositivePredictiveValue:PPV= 

NegativePredictiveValue:NPV=

ROC= Where

sensitivity+ speificity 2



TP

TN    TN+ FN

VI. CONCLUSION ThisworkhasevaluatedtheperformanceoftheK -NearestneighbourAlgorithmwithrespectto confusionmatrixandaccuracy.AccordingtothatKNearestneighbourAlgorithmbasedclassificationapproac hprovidedsignificantlypoorperformance.Itwasfoundtha ttheK-NearestneighbourAlgorithm classifierwascapableofidentifyingNormal,Suspiciousa ndPathologiccondition,fromthenatureofCTGdatawithc omparativelypooraccuracy.If weconsideronlytheprecisionasametric,thenarrivedresul tsprovesthat,eventhoughthemachinelearningbasedmeth odscandistinguishthe NormalCTG

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Therecallortruepositiverate(TP)isthe proportionofpositivecasesthat werecorrectlyidentified • Thefalsepositiverate(FP)istheproportionof negativescasesthatwereincorrectlyclassified aspositive • Thetruenegativerate(TN)isdefinedasthepro portionofnegativescasesthat wereclassifiedcorrectly B. patternsfromtheSuspiciousandPathologicpatter nswithrespecttoprecisionandpathologic,but,they wereincapableofdistinguishingSuspicious.Thati swhywearegettingcomparativelypooraveragepe rformancewhileclassifyingsuspiciousrecordswit hrespecttoprecision.Itisamajorweaknessoftheal gorithmswhichshouldbeovercomesinfuturedesi gn.Onemayaddressthewayto improvethesystemforgettingproperresultswithdi fferent C. classesofCTGpatterns.Onemayconsidermachinel earningbasedmethodtodesigntheCTGdataclassifi cationsystem.Futureworksmayaddresshybridmo delsusingstatisticalandmachinelearningtechnique sforimprovedclassificationaccuracy.

D. E. Infuturework,weplantocollaboratewithobstetriccl iniciansandphysiciansinordertoassessthe computationalresults.

•

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