PredictiveModeling inBiomedicalDataMining andAnalysis
Editedby SudiptaRoy
DepartmentofArtificialIntelligenceandDataScience,JioInstitute, NaviMumbai,Maharashtra,India
LalitMohanGoyal
DepartmentofComputerEngineering,JCBoseUniversityofScience andTechnology,YMCA,Faridabad,India
ValentinaE.Balas
ProfessorofAutomationandAppliedInformatics,AurelVlaicuUniversity ofArad,Arad,Romania
BasantAgarwal
DepartmentofComputerScienceandEngineering,IndianInstitute ofInformationTechnologyKota,Jaipur,Rajasthan,India
MamtaMittal
DelhiSkillandEntrepreneurshipUniversity,NewDelhi,India
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Contributorsxi
AbouttheEditorsxv
Prefacexix
1.Dataminingwithdeeplearninginbiomedicaldata1
KuldeepSinghandJyoteeshMalhotra
1.Introduction1
2.Roleofdeeplearningtechniquesinepilepticseizure detection3
3.Proposedmethodofseizuredetection5
4.Resultsanddiscussion12
5.Conclusions16 References16
2.Applicationsofsupervisedmachinelearningtechniques withthegoalofmedicalanalysisandprediction: Acasestudyofbreastcancer21 KoushalKumarandBhagwatiPrasadPande
1.Introduction21
2.Abriefliteraturesurvey23
3.Datasetandmodusoperandi24
4.Datavisualization30
5.Featureselectionanddimensionalityreduction33
6.Experimentalresultsanddiscussions39
7.Conclusions45 References46
3.Medicaldecisionsupportsystemusingdatamining49
N.L.Taranath,H.R.Roopashree,A.C.Yogeesh,L.M.Darshan,and C.K.Subbaraya
1.Introduction49
2.Medicaldecisionsupportsystem:Areview50
3.OntologicalrepresentationofMDSS53
4.Integratedmedicaldecisionsupportsystem57
5.Conclusionandfutureenhancement62 References63
4.RoleofAItechniquesinenhancingmulti-modality medicalimagefusionresults65
HarmeetKaurandSatishKumar
1.Introduction65
2.Modalities66
3.Fusionprocess67
4.AIbasedfusion70
5.Evaluation73
6.Experimentalresults75
7.Conclusionandfuturescope79 Acknowledgment79 References79
5.Acomparativeperformanceanalysisofbackpropagation trainingoptimizerstoestimateclinicalgaitmechanics83 JyotindraNarayan,SanchitJhunjhunwala,ShivanshMishra, andSantoshaK.Dwivedy
1.Introduction83
2.Methods:Relatedworkanddataset86
3.Backpropagationneuralnetworkandtrainingoptimizers88
4.BPNNimplementation92
5.Resultsanddiscussions94
6.Conclusions101 References102
6.High-performancemedicineincognitiveimpairment: Brain–computerinterfacingforprodromal Alzheimer’sdisease105
H.M.K.K.M.B.Herath,R.G.D.Dhanushi,andB.G.D.A.Madhusanka
1.Introduction105
2.Relatedworks108
3.Methodology109
4.Results115
5.Conclusion119 References120
7.BraintumorclassificationsbygradientandXGboosting machinelearningmodels123 NaliniChintalapudi,GopiBattineni,LalitMohanGoyal, andFrancescoAmenta
1.Introduction123 2.Researchbackground125 3.Methods126 4.Resultsanddiscussions132 5.Conclusions135 Conflictsofinterest135 References135
8.Biofeedbackmethodforhuman–computerinteraction toimproveeldercaring:Eye-gazetracking137
B.G.D.A.Madhusanka,SureswaranRamadass,PremkumarRajagopal, andH.M.K.K.M.B.Herath
1.Introduction137
2.Anatomyofthehumaneye138
3.Overviewofeye-gazetracking140
4.Eye-gazetrackingforhuman–computerinteraction142
5.Proposeddesign143
6.Results147
7.Conclusion151 References152
9.Predictionofbloodscreeningparametersforpreliminary analysisusingneuralnetworks157 AmanKataria,DivyaAgrawal,SitaRani,VinodKarar, andMeetaliChauhan
1.Introduction157
2.Relatedwork158
3.Methodology160
4.Results163
5.Conclusion167 References167
10.Classificationofhypertensionusingan improvedunsupervisedlearningtechniqueand imageprocessing171 UsharaniBhimavarapuandMamtaMittal
1.Introduction171
2.Relatedwork174
3.Methodology175
4.Experimentalresults178
5.Conclusion184 References184
11.Biomedicaldatavisualizationandclinicaldecision-making inrodentsusingamulti-usagewirelessbrainstimulator withanovelembeddeddesign187
V.MilnerPaul,LoitongbamSurajkumarSingh,S.R.BoselinPrabhu, T.Jarin,ShumaAdhikari,andS.Sophia
1.Introduction187
2.Architecturaldesignandcircuitmodeling189
3.Implementationandexperimentalverification193
4.Resultsanddiscussions201
5.Conclusionandfuturedirections202 References204
12.LSTMneuralnetwork-basedclassificationofsensory signalsforhealthyandunhealthygaitassessment207
JyotindraNarayan,SanghamitraJohri,andSantoshaK.Dwivedy
1.Introduction207
2.Datasetcollection209
3.LSTMneuralnetworkmodel209
4.ImplementationofLSTMneuralnetwork215
5.Resultsanddiscussions217
6.Conclusions221 References221
13.Data-drivenmachinelearning:Anewapproachto processandutilizebiomedicaldata225
Kalpana,AdityaSrivastava,andShashankJha
1.Anintroductiontoartificialintelligenceandmachine learninginhealthcare225
2.Challengesandroadblockstobeaddressed231
3.Theneedtoaddresstheseissues238
4.Recommendationsandguidelinesfortheimprovement ofML-basedalgorithms238
5.Applicationsinthepresentscenarios241
6.Futureprospectsandconclusion244 References246
14.Multiobjectiveevolutionaryalgorithmbasedon decompositionforfeatureselectioninmedicaldiagnosis253
SudhansuShekharPatra,MamtaMittal,andOmPrakashJena 1.Introduction253
2.Medicalapplications255
3.Featureselection257
4.Literaturereview261
5.MetaheuristicsandMOO262
6.Multiobjectiveoptimizationproblems(MOOPs)266
7.RoleofEAinMOO272
8.MOEAbasedondecomposition274
9.ApplicationofMOEA/Dinfeatureselectionfor medicaldiagnosis281
10.Experimentalresults286 11.Conclusion289 References289
15.Machinelearningtechniquesinhealthcareinformatics: Showcasingpredictionoftype2diabetesmellitus diseaseusinglifestyledata295 MajidBashirMalik,ShahidMohammadGanie,andTasleemArif 1.Introduction295
2.Machinelearninginhealthcare296
3.Proposedframework302
4.Resultsanddiscussion303
5.Conclusionandfuturescope306 References309
Index313
Contributors
ShumaAdhikari DepartmentofElectricalEngineering,NationalInstituteofTechnology Manipur(NITM),Imphal,India
DivyaAgrawal CSIR-CSIO,Chandigarh,India
FrancescoAmenta ClinicalResearchCentre,SchoolofMedicinalandHealthProducts Sciences,UniversityofCamerino,Camerino;ResearchDepartment,InternationalRadio MedicalCentre(C.I.R.M.),Rome,Italy
TasleemArif DepartmentofInformationTechnology,BGSBUniversity,Rajouri,UTJ&K, India
GopiBattineni ClinicalResearchCentre,SchoolofMedicinalandHealthProducts Sciences,UniversityofCamerino,Camerino,Italy
UsharaniBhimavarapu DepartmentofComputerScienceandEngineering,Koneru LakshmaiahEducationFoundation,Vaddeswaram,AndhraPradesh,India
MeetaliChauhan DepartmentofComputerScienceandEngineering,GulzarInstituteof EngineeringandTechnology(AffiliatedToI.K.G.PunjabTechnicalUniversity,Kapurthala) GulzarGroupofInstitutes,Khanna(Ludhiana),Punjab,India
NaliniChintalapudi ClinicalResearchCentre,SchoolofMedicinalandHealthProducts Sciences,UniversityofCamerino,Camerino,Italy
L.M.Darshan SchoolofCSE,REVAUniversity,Bengaluru,Karnataka,India
R.G.D.Dhanushi FacultyofLivestockFisheries&Nutrition,WayambaUniversityofSri Lanka,Makandura,SriLanka
SantoshaK.Dwivedy DepartmentofMechanicalEngineering,IndianInstituteof TechnologyGuwahati,Guwahati,Assam,India
ShahidMohammadGanie DepartmentofComputerSciences,BGSBUniversity,Rajouri, UTJ&K,India
LalitMohanGoyal DepartmentofComputerEngineering,JCBoseUniversityofScience andTechnology,YMCA,Faridabad,India
H.M.K.K.M.B.Herath FacultyofComputingandIT,SriLankaTechnologicalCampus, Padukka,SriLanka
T.Jarin DepartmentofElectricalandElectronicsEngineering,JyothiEngineeringCollege, Thrissur,India
OmPrakashJena DepartmentofComputerScience,RavenshawUniversity,Cuttack, India
ShashankJha DepartmentofBiotechnology,Dr.AmbedkarInstituteofTechnologyfor Handicapped,Kanpur,UttarPradesh,India
SanchitJhunjhunwala DepartmentofMechanicalEngineering,IndianInstituteof TechnologyGuwahati,Guwahati,Assam,India
SanghamitraJohri DepartmentofMechanicalEngineering,IndianInstituteof TechnologyGuwahati,Guwahati,Assam,India
Kalpana DepartmentofBiotechnology,Dr.AmbedkarInstituteofTechnologyfor Handicapped,Kanpur,UttarPradesh,India
VinodKarar CSIR-CSIO,Chandigarh,India
AmanKataria CSIR-CSIO,Chandigarh,India
HarmeetKaur DCSA,PanjabUniversity,Chandigarh,India
KoushalKumar SikhNationalCollege,Qadian,GuruNanakDevUniversity,Amritsar, Punjab,India
SatishKumar SSGRegionalCentreHoshiarpur,PanjabUniversity,Chandigarh,India
B.G.D.A.Madhusanka SchoolofScienceandEngineering,MalaysiaUniversityofScience andTechnology(MUST),PetalingJaya,Malaysia
JyoteeshMalhotra DepartmentofEngineeringandTechnology,GuruNanakDev UniversityRegionalCampusJalandhar,Punjab,India
MajidBashirMalik DepartmentofComputerSciences,BGSBUniversity,Rajouri,UTJ&K, India
V.MilnerPaul DepartmentofElectricalEngineering,NationalInstituteofTechnology Manipur(NITM),Imphal,India
ShivanshMishra DepartmentofMechanicalEngineering,SardarVallabhbhaiNational InstituteofTechnology,Surat,India
MamtaMittal DelhiSkillandEntrepreneurshipUniversity,NewDelhi,India
JyotindraNarayan DepartmentofMechanicalEngineering,IndianInstituteof TechnologyGuwahati,Guwahati,Assam,India
BhagwatiPrasadPande DepartmentofComputerApplications,LSMGovernmentPG College,Pithoragarh,Uttarakhand,India
SudhansuShekharPatra SchoolofComputerApplications,KIITDeemedtobe University,Bhubaneswar,India
S.R.BoselinPrabhu DepartmentofElectronicsandCommunicationEngineering,Surya EngineeringCollege,Mettukadai,India
PremkumarRajagopal MalaysiaUniversityofScienceandTechnology(MUST),Petaling Jaya,Malaysia
SureswaranRamadass SchoolofScienceandEngineering,MalaysiaUniversityofScience andTechnology(MUST),PetalingJaya,Malaysia
SitaRani DepartmentofComputerScienceandEngineering,GulzarInstituteof EngineeringandTechnology(AffiliatedToI.K.G.PunjabTechnicalUniversity, Kapurthala)GulzarGroupofInstitutes,Khanna(Ludhiana),Punjab,India
H.R.Roopashree DepartmentofCS&E,GSSSITEW,Mysuru,Karnataka,India
KuldeepSingh DepartmentofElectronicsTechnology,GuruNanakDevUniversity Amritsar,Punjab,India
LoitongbamSurajkumarSingh DepartmentofElectronics&Communication Engineering,NationalInstituteofTechnologyManipur(NITM),Imphal,India
S.Sophia DepartmentofElectronicsandCommunicationEngineering,SriKrishna CollegeofEngineeringandTechnology,Kuniyamuthur,India
AdityaSrivastava DepartmentofBiomedicalEngineering,IndianInstituteofTechnology Hyderabad,Sangareddy,Kandi,Telangana,India
C.K.Subbaraya AdichuchanagiriUniversity,Nagamangala,Karnataka,India
N.L.Taranath DepartmentofCS&E,GraphicEraHillUniversity,Dehradun,Uttarakhand, India
A.C.Yogeesh DepartmentofCS&E,GovernmentEngineeringCollege,Kushalnagar, Karnataka,India
AbouttheEditors
Dr.SudiptaRoy isworkingasanassistantprofessorintheArtificialIntelligenceandData ScienceDepartmentatJIOInstitute,NaviMumbai,Maharashtra,India.Priortothat,he wasapostdoctoralresearchassociateatWashingtonUniversityinSt.Louis,MO,United States.HehasreceivedhisPhDinComputerScienceandEngineeringfromtheDepartmentofComputerScienceandEngineering,UniversityofCalcutta,Kolkata,WestBengal, India.Heistheauthorofmorethan50publicationsinrefereedinternationaljournalsand conferenceproceedingspublishedbyIEEE,Springer,Elsevier,andmanyotherpublishers. Hehasauthored/editedfourbooksandmanybookchapters.HeholdsaUSpatentin medicalimageprocessingandhasfiledanIndianpatentinthefieldofsmartagricultural systems.Hehasservedasaregularreviewerformanyinternationaljournalsincluding thosepublishedbyIEEE,Springer,Elsevier,IET,andmanyotherpublishers,andinternationalconferences.Hehasservedasaninternationaladvisorycommitteememberand programcommitteememberofINDIAcom-2020,AICAE-2019,INDIACom-2019,CAAI 2018,ICAITA-2018,ICSESS-2018,INDIACom-2018,ISICO-2017,AICE-2017,andmany otherconferences.Currently,heisservingasassociateeditorof IEEEAccess (IEEE)and InternationalJournalofComputerVisionandImageProcessing (IJCVIP;IGIGlobal)and topiceditorof JournalofImaging (MDPI).Inrecognitionofhisexceptionalcontributions tothe IEEEAccess journalasassociateeditor,theIEEEAccessEditorialBoardandEditorial OfficehonoredhimasanOutstandingAssociateEditorof2020.Hehasmorethan5years ofexperienceinteachingandresearch.Hisfieldsofresearchinterestsarebiomedical imageanalysis,imageprocessing,steganography,artificialintelligence,bigdataanalysis, machinelearning,andbigdatatechnologies.
Dr.LalitMohanGoyal hascompletedPhDinComputerEngineeringfromJamiaMillia Islamia,NewDelhi,India,MTech(Honors)inInformationTechnologyfromGuruGobind SinghIndraprasthaUniversity,NewDelhi,India,andBTech(Honors)inComputerEngineeringfromKurukshetraUniversity,Kurukshetra,India.Hehas17yearsofteaching experienceintheareasoftheoryofcomputation,parallelandrandomalgorithms,distributeddatamining,andcloudcomputing.Hehascompletedaprojectsponsoredbythe IndianCouncilofMedicalResearch,Delhi.Hehaspublishedandcommunicatedmore than40researchpapersinSCI,SCIE,andScopus-indexedjournalsandattendedmany workshops,FacultyDevelopmentPrograms,andseminars.Hehasfiledninepatentsin theareaofartificialintelligenceanddeeplearning,outofwhichfourhavebeengranted andothersarepublishedonline.Heisareviewerofmanyreputedjournalsandconferences.HeisaserieseditorforCRCPress,Taylor&Francis,andhaseditedmanybooks
forElsevierandSpringer.Presently,heisworkingintheDepartmentofComputerEngineering,J.C.BoseUniversityofScienceandTechnology,YMCA,Faridabad,India.
Prof.ValentinaE.Balas iscurrentlyfullprofessorintheDepartmentofAutomaticsand AppliedSoftwareattheFacultyofEngineering,AurelVlaicuUniversityofArad,Romania. SheholdsaPhDcumlaudeinAppliedElectronicsandTelecommunicationsfromPolytechnicUniversityofTimisoara.Dr.Balasistheauthorofmorethan400researchpapers inrefereedjournalsandinternationalconferences.Herresearchinterestsareinintelligent systems,fuzzycontrol,softcomputing,smartsensors,informationfusion,andmodeling andsimulation.Sheistheeditor-in-chiefofthe InternationalJournalofAdvancedIntelligenceParadigms (IJAIP)and InternationalJournalofComputationalSystemsEngineering (IJCSysE),editorialboardmemberofseveralnationalandinternationaljournals,and expertevaluatorfornational/internationalprojectsandPhDtheses.Dr.BalasisthedirectorofIntelligentSystemsResearchCentreinAurelVlaicuUniversityofAradanddirector oftheDepartmentofInternationalRelations,ProgramsandProjectsinthesameuniversity.SheservedasthegeneralchairfornineeditionsoftheInternationalWorkshoponSoft ComputingandApplications(SOFA)organizedduringtheperiod2005–2020andheldin RomaniaandHungary.Dr.Balasparticipatedinmanyinternationalconferencesasorganizer;honorarychair;sessionchair;memberofthesteering,advisory,orinternational programcommittees;andkeynotespeaker.Recently,shewasworkingonanationalprojectwithEUfundingsupport“BioCell-NanoART ¼ NovelBio-inspiredCellularNanoArchitectures—ForDigitalIntegratedCircuits,”3MEurofromtheNationalAuthority forScientificResearchandInnovation.SheisamemberoftheEuropeanSocietyforFuzzy LogicandTechnology(EUSFLAT),memberoftheSocietyforIndustrialandApplied Mathematics(SIAM),aseniormemberofIEEE,memberofTechnicalCommittee—Fuzzy Systems(IEEEComputationalIntelligenceSociety),chairoftheTaskForce14inTechnical Committee—EmergentTechnologies(IEEECIS),andmemberofTechnicalCommittee— SoftComputing(IEEESMCS).Dr.BalaswaspastvicepresidentoftheInternationalFuzzy SystemsAssociation(IFSA)Council(2013–2015),isajointsecretaryofthegoverning counciloftheForumforInterdisciplinaryMathematics(FIM,amultidisciplinaryacademicbodybasedinIndia),andisarecipientofthe“TudorTanasescu”Prizefromthe RomanianAcademyforcontributionsinthefieldofsoftcomputingmethods(2019).
Dr.BasantAgarwal isworkingasanassistantprofessorattheIndianInstituteofInformationTechnologyKota(IIIT-Kota),India,whichisaninstituteofnationalimportance.He holdsaPhDandMTechfromtheDepartmentofComputerScienceandEngineering, MalaviyaNationalInstituteofTechnologyJaipur,India.Hehasmorethannineyearsof experienceinresearchandteaching.Hehasworkedasapostdocresearchfellowatthe NorwegianUniversityofScienceandTechnology(NTNU),Norway,undertheprestigious EuropeanResearchConsortiumforInformaticsandMathematics(ERCIM)fellowshipin 2016.HehasalsoworkedasaresearchscientistatTemasekLaboratories,National
UniversityofSingapore(NUS),Singapore.Hisresearchinterestsareinartificialintelligence,cyber-physicalsystems,textmining,naturallanguageprocessing,machinelearning,deeplearning,intelligentsystems,expertsystems,andrelatedareas.
Dr.MamtaMittal isworkingasprogramheadandassociateprofessor(DataAnalyticsand DataScience)inDelhiSkillandEntrepreneurshipUniversity(undertheGovernmentof NCTDelhi),NewDelhi,India.ShereceivedherPhDinComputerScienceandEngineering fromThaparUniversity,Patiala;MTech(Honors)inComputerScienceandEngineering fromYMCA,Faridabad;andBTechinComputerScienceandEngineeringfromKurukshetraUniversity,Kurukshetra,in2001.Shehasbeenteachingforthelast18yearsandspecializesindatamining,machinelearning,DBMS,anddatastructure.Dr.Mittalisa lifetimememberofCSIandhaspublishedmorethan80researchpapersinSCI,SCIE, andScopus-indexedjournals.Sheholdsfivepatents,twocopyrightsgranted,andthree morepublishedpatentsintheareasofartificialintelligence,IoT,anddeeplearning.Dr. Mittalhasedited/authoredmanybookswithreputedpublisherslikeSpringer,IOSPress, Elsevier,andCRCPressandisworkingonaDST-approvedproject“Developmentof IoT-BasedHybridNavigationModuleforMid-sizedAutonomousVehicles”witharesearch grantof25lakhs.Currently,sheisguidingPhDscholarsintheareasofmachinelearning, computervision,anddeeplearning.Dr.Mittalisaneditorialboardmemberforpublishers likeInderscience,BenthamScience,Springer,andElsevier,andhashandledspecialissues andchairedanumberofconferences.SheisbookserieseditorofInnovationsinHealth InformaticsandHealthcare:UsingArtificialIntelligenceandSmartComputingand anotherseriesEdgeAIinFutureComputingforCRCPress,Taylor&Francis,UnitedStates. Sheisassociateeditor,advisorymember,andeditorforSpringerjournals, Dyna (Spain), andElsevierjournals,respectively.
Preface
Machinelearning(ML)techniquesareusedaspredictivemodelsformanyapplications includingthoseinthefieldofbiomedicine.Thesetechniqueshaveshownimpressive resultsacrossavarietyofdomainsinbiomedicalengineeringresearch.Biologyandmedicinearedata-richdisciplines,butthedataarecomplexandoftennotproperlyunderstood.Mostbiomedicaldataarecategorizedintostructured,semi-structured,and unstructuredtypeswithveryhighvolume.Thevolumeandcomplexityofthesedatapresentnewopportunities,butalsoposenewchallenges.Automatedalgorithmsthatextract meaningfulpatternscouldleadtoactionableknowledgeandchangehowwedevelop treatments,categorizepatients,orstudydiseases,allwithinprivacy-criticalenvironments.Thisbookaddressestheissuesdescribedtopredictandmodelbiomedicaldata miningandanalysis.Thebookhasbeenorganizedinto15chapters.
Chapter1 titled“DataMiningwithDeepLearninginBiomedicalData”presentsatimedomainstudyofEEGsignalstodetectvariousneurologicaldisorderswithaspecificfocus onepilepsy.Thepresentedconvolutionalneuralnetwork(CNN),longshort-termmemory network(LSTM),andCNN-LSTMhybridmodelswereusedtodetectseizureactivitiesin preciselyfilteredEEGsegments.Theexperimentalresultsrevealthesuitabilityofthe CNN-LSTMhybridmodelforaccurateandpromptdetectionofepilepticseizureswith anaccuracyof98%,sensitivityof98.48%,andspecificityof99.19%,sothatpatientscould besavedfrommajorinjuriesorsuddenexpecteddeaths.Thesemodelscanbeusefulin thedetectionofvariousdiseasesordisorderssuchasschizophrenia,Parkinson’sdisease, andtheidentificationofbreastcancerandbone-orskin-relateddiseases.
Chapter2 titled“ApplicationsofSupervisedMachineLearningTechniqueswiththe GoalofMedicalAnalysisandPrediction:ACaseStudyofBreastCancer”analyzestheWisconsinBreastCancerDiagnosisdatasetforidentifyingessentialfeaturesandassessingthe performanceofsomepopularmachinelearning(ML)classifiersinbreastcancerprediction.Thedatasetisfirstcleanedbyeliminatingnon-numericalvaluesandnormalizingthe data.Theprocesseddataarethenvisualizedtograspthehiddenpatternsandnonessentialattributesaretrimmed.EightdifferentMLmodelsaretrainedandtestedover therefineddataforpredictionofthetwotumorclasses.Thepresentedstudyidentified vitalfeaturesthatweremust-havesfortheanalysis,andtheempiricalresultsinvestigated thesuperiorityofparticularMLclassifiersovertheothers.
Chapter3 titled“MedicalDecisionSupportSystemUsingDataMining”describeshow amedicaldecisionsupportsystemcansupportthemedicaldecision-makingprocessesat bothclinicalanddiagnosticlevels.Toprovideanerror-freeandaccurateservice,clinicians
mustapplyrelevantcomputer-basedinformationanddecisionsupportsystems.Decision supportsystemscanbedesignedasasystembasedonknowledgeorasystembasedon learning.Human-engineeredmappingstosuggestionsbasedonbestmedicaltreatments andpatientdataareknownasknowledge-basedsystems.Learning-basedsystemsutilize datamining,statistics,andMLapproachestomapthesystem.Integrateddecisionsupportincorporatesboththesystemsofknowledgeandlearningtosolvetheproblemof presenceofpartialinformationinarealisticsituation.Thiseffortaimstoassistphysicians medicallyandtoapplythemedicineprescriptionspecifically.Theapproachmaybe utilizedforquery-basedapplications,onlinewebbrowserapplications,ormobileapplicationsonnumerousterminalinterfaces.
Chapter4 titled“RoleofAITechniquesinEnhancingMulti-ModalityMedicalImage FusionResults”outlinesthebenefitsofusingAImethodsformedicalimagefusionofdifferentmodalities.Themodalitycanbecomputedtomography,magneticresonance-T1, magneticresonance-T2,andPositronemissiontomographydependingonthesuspected malignantregion.Theaimoffusionistocollaborateeachmodality’sbestinformationinto asingleimagecalledafusedimage.Thischapteraddressesthemulti-modalitymedical imagefusionusingAItechniqueslikeFuzzyLogicandAdaptiveNeuro-FuzzyInference System(ANFIS).ThestudyrevealsthattheAItechniquesnotonlygivebetterresults buttheirlearningcapabilitieswilllikelymakethefutureworkself-driven.
Chapter5 titled“AComparativePerformanceAnalysisofBackpropagationTraining OptimizerstoEstimateClinicalGaitMechanics”indicatesthattheclinicalgaitanalysis ofhealthypeopleofdifferentagegroupsplaysasignificantroleintheearlyestimation ofdifferentphysiologicalandneurologicaldisorders.However,duetocomplicateddata acquisitionsetupsandin-personrequirements,theestimationofthegaitanalysishas beenquitetoughtofollow.Toavoidsuchissues,aML-basedapproachhasbeenproposed inthisworktoestimatethebiomechanicalgaitparameters.Threebackpropagationneural networkmodelswithLevenberg-Marquardtmethod,resilientbackpropagationmethod, andgradientdescentmethodoptimizershavebeendesignedtoestimatethejointangles, jointmoments,andgroundreactionforcesinthesagittalplane.Thedatasetusedinthe neuralnetworkmodelshasbeentakenfromanopen-sourcerepository.Theanthropometric,biological,andspatiotemporalparametersof50differentsubjectshavebeen exploitedasinputdataset.
Chapter6 titled“High-PerformanceMedicineinCognitiveImpairment:Brain–Computer InterfacingforProdromalAlzheimer’sDisease”suggeststhatAlzheimer’sdiseaseisfrequentlymisdiagnosedasnormalagingbecauseithasalwaysbeendifficulttodetectearly on.Mildcognitiveimpairment(MCI)canbeidentified,butthereislittlethatcanbe doneatthattimebecausenomedicinecanreversetheeffectofMCI;instead,itcanonly slowdowntheprogression.Alzheimer’sdiseaseisdifficulttodiagnosemedically,especiallyinitsearlystages.Asaresponse,amethodforearlydiagnosisofAlzheimer’sdiseaseisurgentlyneededevennow.Inthischapter,theauthorshaveproposedastrategy fordetectingAlzheimer’sdiseaseinitsearlystageusingnoninvasivebrain-computer interfacetechnology.Electroencephalography(EEG)brainwavepatternswereusedfor
threegroups(Alzheimer’sdisease—AD,mildcognitiveimpairment—MCI,andhealthy subjects—HS)oftestsubjectsinthisresearch.Theproposedframeworkwasevaluated with46testsubjects,withanaccuracyof86.47%andaprecisionof0.801.
Chapter7 titled“BrainTumorClassificationsbyGradientandXGBostingMachine LearningModels”describestheuseoftheboosting-typeMLalgorithmstoevaluatethe modelperformanceparameters.ModelperformanceisvalidatedusingK-foldmethods andpreliminaryresultsindicatethattheXGboostingalgorithmyieldsthehighestclassificationaccuracy.Evaluationsofthistypearelargelysupportiveofbiomedicalimaging studiesandthereisscopeforfuturestudiesusingotherclassificationmodelsforachieving thehighestpredictionaccuracy.
Chapter8 titled“BiofeedbackMethodforHuman–ComputerInteractiontoImprove ElderCaring:Eye-GazeTracking”proposeshowphysiologicalmethodsofeye-gazetrackingcouldbeusedtodesignanddevelopnaturaluserinteractiontechniques.Ahuman user’stacitintentiontousephysiologicalsignalsforthedomesticarea’srequiredactivities/requirementsmaybeunderstoodbyutilizingnonverbalcontacttodefinetheuser’s intentiontousephysiologicalsignalsforthedomesticarea’snecessaryactivities/requirements.Toachievegoodaccuracyandrobustness,traditionalgazemonitoringsystems dependonexplicitinfraredlightsandhigh-resolutioncameras.Recentadvancements inmobiledevices,aswellasanincreasinginterestinrecordingnormalhumanbehavior, haveshownthattrackingeyemotionsinanon-restrictedenvironmentcouldyieldpromisingresults.
Chapter9 titled“PredictionofBloodScreeningParametersforPreliminaryAnalysis UsingNeuralNetworks”describesvarioustechniquesusedinthepredictionofblood parameters.Thepredictionofbloodscreeningtestfeaturesusingthebackpropagation neuralnetworkispresentedindetail.Thefeaturesusedinthischapterwerefibrinogen andglobulin.Thenormalrangesoffibrinogenandglobulinare2–4g/Land20–35g/L, respectively.Fibrinogenisa glycoprotein thatcirculatesinthebloodofallvertebrates. Itisobservedfromtheresultsthatthepredictionaccuracyforfibrinogenisbetterthan thatforglobulin.Toincreasetheaccuracyofthepredictionforglobulin,thetraining parametersandactivationfunctionsmustbemodified.
Chapter10 titled“ClassificationofHypertensionUsinganImprovedUnsupervised LearningTechniqueandImageProcessing”presentsanimprovednearestneighbordistanceclusteringalgorithmbyrecognizingthelesionspresentintheretina.Thecurrent approachidentifiesthesymptomsassociatedwithretinopathyforhypertensionandclassifiesthehypertensiveretinopathy.Thischapterprovidesanassessmentofthehypertensiveretinopathyrecognitiontechniquesthatapplyarangeofimageprocessing proceduresusedforfeatureextractionandclassification.Thechapteralsospecifiesthe existingopendatabases,containingeyefundusimages,whichcanbeusedforhypertensiveretinopathyresearch.
Chapter11 titled“BiomedicalDataVisualizationandClinicalDecision-Makingin RodentsUsingaMulti-usageWirelessBrainStimulatorWithaNovelEmbeddedDesign” describesindetailthecompletedesign,biomedicaldatavisualization,andmodeling
aspectsofthestimulatordevice.Thefeasibilityofthisdeviceissuccessfullytestedin invivoandinvitrostagesforaperiodofmorethanamonth.Thisembeddeddesign hasbeendevelopedtakingintoaccountcost-effectiveness,user-friendliness,andprecision,whicharethemainfocusofthischapter.Thebrain-computerinterfacecanbeuseful intakingeffectiveclinicaldecision-makingatanearlystage.However,thereislimited researchinthisareasofar.Therefore,alltheeffortsinthisdirectionareextremelyimportantfornumerousyoungflourishingspecialists,andaspirationstowardthebraincomputerinterface.
Chapter12 titled“LSTMNeuralNetwork-BasedClassificationofSensorySignalsfor HealthyandUnhealthyGaitAssessment”describesthemodelingofthelongshort-term memory(LSTM)deepneuralnetworkmodelanditsimplementationtoclassifyhealthy andunhealthygaitbasedonasensorydataset.Thereferencesensorydatasetof22subject samples(11healthyand11withkneepathology)istakenfromtheUCIIrvineMachine LearningRepository.Twodifferentoptimizers,namelyStochasticGradientDescentand Adam,havebeenexploitedinthedesignedLSTMmodelwithdifferentsetsoflearning hyperparameters.Theclassificationresultsoftheproposeddeeplearningmodelwith bothoptimizershavebeencomparedwitheachotherusingseveralperformancemeasureslikeprecision,recall,andF1score.
Chapter13 titled“Data-DrivenMachineLearning:ANewApproachtoProcessand UtilizeBiomedicalData”includesastudyofpreciseandaccuratediagnostictoolstoease thepressureonmedicalpersonnel,simultaneouslyenhancingefficiency.Thischapter exploresthedevelopmentofartificialneuralnetworkbaseddiagnostictoolsthatfocus onthechallengesdescribedpreviously.Abriefoverviewofthecurrentscenariosand futureprospectsofMLinbiomedicineisalsopresented.
Chapter14 titled“MultiobjectiveEvolutionaryAlgorithmBasedonDecompositionfor FeatureSelectioninMedicalDiagnosis”presentsamathematicalmodelofamultiobjectiveevolutionaryalgorithmbasedondecomposition(MOEA/D)anditsapplication infeatureselectioninmedicaldiagnosis.Mostofthemedicaldatasetsarehighdimensionalinnatureandsothereisaneedforoptimalfeatureselection,whichisadifficult problem.Thenegativeinfluencemaybeduetothepossibilityofirrelevantormanyredundantfeatures.Intelligentmodelsincludingclassification,clustering,regression,and boostingtechniquesarehelpfulinextractingusefulknowledge.Theperformanceof theMOEA/Dmethodiscomparedwiththatofstate-of-the-artmulti-objectiveoptimizationmethodswhenappliedtomostofthedatasets.
Chapter15 titled“MachineLearningTechniquesinHealthcareInformatics:ShowcasingPredictionofType2DiabetesMellitusDiseaseusingLifestyleData”focusesontherole oftheMLparadigmsinhealthcareanalyticsandpresentstheimplementationofthe frameworkfordevelopingMLmodelsfortype2diabetesmellitus(T2DM)disease.Inthis chapter,lifestyleindicatorsratherthanclinical/pathologicalparametershavebeenused forthepredictionofT2DM.Thestudyinvolvesdifferentexpertslikediabetologists,endocrinologists,dieticians,andnutritionistsforselectingthecontributinglifestyleparameterstopromotehealthandmanagediabetes.Thestudyaimstodevelopanintelligent
knowledge-basedsystemforthepredictionofT2DMwithoutconductingclinicaltests.It cansavethepatientunduedelayscausedbyunnecessaryreadmissionsandpathological testsinhospitals.TheproposedworkemphasizestheuseofMLtechniques,namelyKnearestneighbor(KNN),logisticregression(LR),naı¨veBayes(NB),supportvector machine(SVM),decisiontree(DT),randomforest(RF),andartificialneuralnetwork (ANN),forthepredictionofT2DMdisease.TheRFtechniqueattainedthehighestaccuracyof93.56%followedbyDT,LR,SVM,NB,ANN,andKNNwithaccuraciesof92.70%, 91.41%,90.98%,89.27%,87.98%,and84.54%,respectively.
WearegratefultoElsevier,especiallyChrisKatsaropoulos,SeniorAcquisitionsEditor, forprovidingustheopportunitytoeditthisbook.
SudiptaRoy Maharashtra,India
LalitMohanGoyal Faridabad,India
ValentinaE.Balas Romania
BasantAgarwal Jaipur,India
MamtaMittal NewDelhi,India
