Machine Learning for Healthcare: Handling and Managing Data


Fundamentals of Machine LearningIntroductionData in Machine LearningThe Relationship between Data Mining, Machine Learning, and Artificial IntelligenceApplications of Machine LearningMachine Learning: The ExpectedMachine Learning: The UnexpectedTypes of Machine LearningSupervised LearningSupervised Learning Use CasesUnsupervised LearningTypes of Unsupervised LearningClusteringAssociation RuleUnsupervised Learning Use CaseReinforcement Learning (RL)ConclusionReferencesMedical Information SystemsIntroductionTypes of Medical Information SystemsGeneral Medical Information SystemsSpecific Medical Information SystemsTypes of General Medical DataNumerical DataTextual DataCategorical DataImaging DataHistory of Medical Information SystemsCollection of MIS Data through Various PlatformsTraditionalElectronicDiagnosis and Treatment of Disease through MIS DataConclusionReferencesThe Role of Metaheuristic Algorithms in HealthcareIntroductionMachine Learning in HealthcareHealth Information System FrameworkPrivacy and Security of DataBig Data Analytics in Disease DiagnosisThe Metaheuristic Algorithm for HealthcareConclusionReferencesDecision Support System to Improve Patient CareIntroductionRelated WorkFeature SelectionEntropy FormulaExperimental SetupConclusionReferencesEffects of Cell Phone Usage on Human Health and Specifically on the BrainIntroductionBackgroundRadiation Produced by a Mobile PhoneMATLAB ToolsProblem StatementResearch ObjectiveState-of-the-Art Research and TechnologyDiscussion of ToolsMethodologyQuantitative ApproachDesign ResearchMethod of Data CollectionSampling TechniqueSample SizeInstrument for Data CollectionResearch ModelK-Means ClusteringResult and DiscussionConclusionReferencesFeature Extraction and Bio SignalsIntroductionFeature ExtractionCommon Spatial PatternsAdaptive Common Spatial PatternsAdaptive CSP PatchesCanonical Correlation AnalysisBand Power FeaturesAdaptive Band Power FeaturesTime Point FeaturesTime Points with Adaptive XDAWNFeature Selection and its ApproachesFilter ApproachWrapper ApproachConclusionReferencesComparison Analysis of Multidimensional Segmentation Using Medical Health-Care InformationIntroductionLiterature ReviewStatic Structure of Literature Review with Another Research ComparisonMethodologyOriginal Result of Image Testing in Binary TransformationHigh Dimension Structured GraphsGrab-CutAlgorithmResult Comparison and DiscussionConclusionAcknowledgmentsReferencesDeep Convolutional Network Based Approach for Detection of Liver Cancer and Predictive Analytics on CloudIntroductionTypes of Liver DiseasesMedical Images and Deep LearningMicro-Service ArchitectureIntegration of NVDIA GPU for Deep Learning on CloudPresenting the Sockets and Slots for ProcessorsClock Details of Deep Learning ServerThreads for Deep Learning–Based ComputationsAvailable Hard Disk for UseMemoryOverall Details of Used Computing Environment with Deep Convolutional NetworksDeep Learning for Liver Diagnosis with the Projected ModelProposed Model and OutcomesConclusionReferencesPerformance Analysis of Machine Learning Algorithm for Healthcare Tools with High Dimension SegmentationIntroductionLiterature ReviewMethodologyProposed FrameworkLight Field Toolbox for MATLABHigh Dimensional Light Field Segmentation MethodHigh Dimensional Structured GraphsHigh Dimension Structured GraphsGrab-CutImage Testing ValueImage Testing ResultGraph Cut Value for B/W ImageImage Testing ValueImage Testing ResultAlgorithmResult and DiscussionConclusionFuture WorkAcknowledgmentReferencesPatient Report Analysis for Identification and Diagnosis of DiseaseIntroductionData VariabilityStructured DataHuman Generated DataMachine Generated DataSemi-Structured DataUnstructured DataComparison of Structured, Unstructured Data, and Semi-StructuredData Collection of DiseasesEMR Data Collection through eHealth DevicesSemantic Data Extraction from Healthcare WebsitesPatient ChatbotsStructured DataConsistency and Quality of Structured DataPredictive Models for AnalysisRegression TechniquesMachine Learning TechniquesAlgorithmsNaïve BayesSupport Vector MachineLogistic RegressionDecision TreesUse CasesCleveland ClinicProvidence HealthDartmouth HitchcockGoogleSemi-Structured DataSemantic ExtractionWeb Mantic ExtractionUse CasesUnstructured DataFinding Meaning in Unstructured DataExtraction of DataText ExtractionImage ExtractionChallenges of Data Extraction from PDFsVideo ExtractionSound ExtractionAlgorithmsNatural Language ProcessingNaïve BayesDeep LearningConvolutional Neural NetworkPhenotyping AlgorithmsUse CasesConclusionReferencesStatistical Analysis of the Pre- and Post-Surgery in the Healthcare Sector Using High Dimension SegmentationIntroductionMethodologySampling TechniquesSample Data and SizeLight Field Toolbox for MATLABHigh Dimensional Light Field Segmentation MethodSupport Vector Machine (SVM)-Dimentional SVM GraphsStatistical TechniqueResult and DiscussionConclusionFuture WorkReferencesMachine Learning in Diagnosis of Children with DisordersIntroductionDown Syndrome (DS)Sensory Processing Disorder (SPD)Autism Spectrum Disorder (ASD)Aims and OrganisationExisting Tools for Diagnosis of DS, SPD, and ASDExisting Tools of DS DiagnosisExisting Tools of SPD DiagnosisExisting Tools for ASD DiagnosisMachine Learning Applied for Diagnosis of DS, SPD, and ASDMachine Learning Case Studies of DS, SPD, and ASDMachine Learning (ML) Case Study for DSMachine Learning Case Study of SPDMachine Learning Case Study for ASDConclusionReferencesForecasting Dengue Incidence Rate in Tamil Nadu Using ARIMA Time Series ModelIntroductionLiterature ReviewFindingsMethods and MaterialsStudy AreaSnapshot for DatasetProposed ModelEstimate and Develop the ModelResults and DiscussionsConclusionAcknowledgmentReferences
 
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