Index

Big Data Analytics and Intelligence: A Perspective for Health Care

ISBN: 978-1-83909-100-1, eISBN: 978-1-83909-099-8

Publication date: 30 September 2020

This content is currently only available as a PDF

Citation

(2020), "Index", Tanwar, P., Jain, V., Liu, C.-M. and Goyal, V. (Ed.) Big Data Analytics and Intelligence: A Perspective for Health Care, Emerald Publishing Limited, Leeds, pp. 281-289. https://doi.org/10.1108/978-1-83909-099-820201022

Publisher

:

Emerald Publishing Limited

Copyright © 2020 Emerald Publishing Limited


INDEX

Accuracy
, 51

Activation functions
, 43

Actuators
, 140, 148

Adaptive boosting (AdaBoost)
, 48–49

Administrative data
, 79

Affordable Care Act
, 120

Affymetrix algorithm
, 131

Air quality
, 99–101

Alcoholism
, 231

Amazon Web Services
, 21

American Recovery and Reinvestment Act (ARRR)
, 117

American Telemedicine Association (ATA)
, 247

Analytical tools
, 4

Annual Health Survey (AHS)
, 63

Apache Pig
, 21

Apollo TeleHealth Services (ATHS)
, 250

Arduino
, 142

Artificial intelligence (AI) (see also Business intelligence (BI))
, 32, 57, 66, 94, 207, 224

Artificial neural network (ANN)
, 234–235, 267–269

ATMEGA328P microcontroller
, 141–142, 144

Automatic tomography
, 174

Autumn eats
, 229–230

Averaging
, 49–50

Bagging technique
, 40, 49

“Banyan of Knowledge” system
, 56

Barometric pressure
, 102

Base learners (see First-level learners)

Bayes theorem
, 35, 270–271

Bell’s palsy
, 174, 176

Beta-blockers
, 206

Big data (BD)
, 17–18, 116, 207, 264

anatomy
, 123–126

definitions in health sector
, 18

methods and technology progress in
, 118–120

needs in health sector
, 18–19

Big data analytics (BDA) (see also Computer-aided big healthcare data analytics; Predictive big data analytics in healthcare)
, 17–18, 56, 59–61, 66–67, 118

achievements
, 71

analysis
, 62–71

applications in health care
, 4–15

challenges
, 23–24, 71–72

concept and emergence of big data
, 59

curing cancer
, 9–10

data as fuel of modern economy
, 57–59

EHRs
, 19

EMRs
, 19

health care analytics environment
, 19

historical perspective
, 65–66

and intelligence
, 2–4

internet of things
, 20

need for security and a mechanism to reducing fraud
, 11–12

objectives
, 61

opportunities in health through BDA use
, 21–23

research methodology
, 61

sensor data
, 19–20

sources of data, methods, and challenges
, 4–6

strategies to overcoming challenges of BDA in health sector
, 24–25

techniques, tools, and technologies in health sector
, 20–21

Big Data to Knowledge (BD2K)
, 119, 127

Big Healthcare Data (BHD)
, 119

BMP180 Breakout
, 102

Body mass index (BMI)
, 272

Boosting
, 48–49

Bootstrap aggregation (see Bagging technique)

Botulinum toxin
, 177

Breast cancer (BC)
, 190

related work on BC prediction
, 190–192

Breastfeeding
, 231

Brownian movement
, 101

Business intelligence (BI)
, 153, 155

contribution
, 154–155

data warehouse design
, 160–161

dataset
, 161–162

ETL process
, 163

evolution
, 156

in healthcare industry and benefits
, 156–157

implementation
, 167–170

literature survey
, 157–159

problem identification
, 159

vision
, 154

workflow
, 159

Business models of telehealth
, 249–252

C4.5 decision tree algorithm technique
, 275

Cadmium Sulfide (CdS)
, 143

Cancer (see also Breast cancer (BC))
, 227

Carbohydrates
, 226, 231

deficiency
, 231

Carbon filter
, 100

Carbs (see Carbohydrates)

CART techniques
, 40

Cassandra
, 84

Cells
, 224–225

Central facial palsy
, 174

Chi-square

algorithm
, 38

test
, 210

Chip
, 142

Chronic facial paralysis
, 177

Chronic kidney disease (CKD)
, 276

Citizen-consumer
, 57

City Block Distance (see Manhattan distance)

Classification algorithms

activation functions
, 43

bagging
, 49

boosting
, 48–49

chi-square algorithm
, 38

CNN and RNN
, 47

combination methods
, 49–50

cost function
, 41–42

data mining in health care
, 32–33

decision trees
, 37–38

E-health
, 52–53

ensemble learning
, 47–48

gradient descent algorithm
, 47

KNN
, 33–34

linear activation function
, 43

logistic regression
, 40–41

model evaluation
, 50–52

Naïve Bayes algorithm
, 34–35

neural networks
, 42–43

pruning
, 39

random forest algorithm
, 39–40

ROC curve
, 52

sigmoid function
, 41

SVM
, 35–37

Clinical decision support (CDD)
, 116

Clinical trials
, 80

Cloudera
, 21

Cluster analysis
, 20

Collaborative Assessment and Recommendation Engine (CARE)
, 131

Combination methods
, 49–50

Committee-based learning
, 48

Complex carbohydrates
, 226

Computational fluid dynamics
, 59

Computer-aided big healthcare data analytics (see also Smart nursery with health monitoring system)

anatomy of big data
, 123–126

benefits of BHD analytics
, 126–127

BHD applications in real clinics
, 127–131

methods and technology progress in big data
, 118–120

motivation
, 120–123

Conductivity sensor
, 101

Confusion matrix
, 51–52

Congestive heart failure (CHF)
, 47

Convolution
, 235

Convolution(al) neural networks (CNNs)
, 47, 235–237

Coronary artery disease (CAD)
, 206

Correlation matrix
, 154, 159, 167

Cosine distance
, 34

Cost function
, 41–42

CRDC tool
, 10

Cross-facial re-innervation processes
, 178

Data

data-citizens
, 57

data-driven economy
, 57

as fuel of modern economy
, 57–59

mining
, 20, 32–33, 66, 264

quality, structure, and accessibility
, 22

set
, 104

Data warehouse (DW)
, 154

DCM education tool
, 210

Decision trees
, 37–39, 158–159, 168, 272

Deep learning
, 42, 234–238

Deming’s PDCA Cycle
, 61

Descriptive analytics
, 56, 67

DHT11 module
, 143, 146

Diabetes (see Diabetes mellitus (DM))

Diabetes distress (DD)
, 210

Diabetes management self-efficacy (DMSE)
, 210

Diabetes mellitus (DM)
, 204, 227

age group and gender distribution
, 212

experimental setup
, 211

future scope, applications, and limitations
, 217–220

insulin consumption
, 213–216

literature survey/previous findings
, 209–211

novelty in work
, 216–217

recommendations
, 220

study and analysis
, 211

Diagnostic analytics
, 56

Dietary fiber
, 227

Digital disease surveillance
, 80

Dimension table
, 160

Disease level of patient
, 176

District Level Household Survey (DLHS)
, 63

Doctor Insta
, 252

Dynamic programming
, 66

E-health
, 13, 52–53

Education
, 14

Electrocardiogram (ECG)
, 116

Electroencephalogram (EEG)
, 116

Electromyography (EMG)
, 116, 174

Electronic health records (EHR)
, 2, 6–7, 19, 76, 83, 116, 156, 248, 264

Electronic medical records (EMR)
, 19, 77–78, 116

Electronic patient records (EPRs)
, 118

Electronic wellbeing records (EWRs)
, 117

Emerging technological ecosystem
, 66

Enhanced Multiclass Support Vector Machine (EMSVM)
, 191

Ensemble

learning
, 47–48

model
, 275

Entropy
, 37

Error matrix (see Confusion matrix)

Euclidean distance
, 33–34

European Union (EU)
, 59

Evidence-based decision-making
, 22

Executive Information Systems (EIS)
, 156

Expression quantitative trait loci (EQTLs)
, 131

Extraction, transformation, and loading process (ETL process)
, 155, 163

eXtreme Gradient Boosting algorithm (XGBoost algorithm)
, 49

ExxonMobil Research and Engineering Company (EMRE)
, 58

F1 score
, 52

Facial palsy
, 173–175

comparative study of existing solution
, 178–181

levels
, 185

literature survey
, 175–176

problem identification
, 177

proposed solution
, 181–183

pros and cons of solution
, 183

Facial paralysis, initial surgery for
, 177

Fact table
, 160

Failure of heart (see Congestive heart failure (CHF))

False negative (FN)
, 51

False positive (FP)
, 51

Fat(s)
, 226

deficiency of
, 231–232

fat-soluble vitamins
, 226

Feature

ranking
, 193

selection
, 190

Filtering
, 105

First-level learners
, 50

Flaccid paralysis
, 177

Flattening
, 237

Food

choices
, 223

dyes
, 227

effect of weather
, 228

role and value of nutrients
, 224–227

security
, 230

Forest optimization algorithm (FOA)
, 191

Fraud detection
, 22

Frequency table
, 37

G1 Dispensaries
, 251

GDC tool
, 9

GEneralizable Medical Information Analysis and Integration System (GEMINI)
, 129

Genetic algorithm (GA)
, 190

Genome Analysis ToolKit (GATK)
, 131

Genome-wide association study (GWAS)
, 131

Genomics
, 78

Gestational diabetes
, 204

Gini importance
, 40

Gini index
, 38

Global Pulse project
, 119

Glocal Digital Dispensaries
, 250–251

Glocal Healthcare Systems Private Limited
, 250–251

Goodness of split criterion
, 272

Government agencies
, 13

Gradient boosting
, 48

Gradient descent algorithm
, 42, 47

Graph analytics
, 21

Gross data product
, 58

Gross domestic product (GDP)
, 121

Grove–Gas Sensor
, 99–100

Hadoop
, 21

Hadoop distributed file system (HDFS)
, 66, 84

Hamming distance
, 33–34

Health Information Technology (HIT)
, 117, 248

Health Insurance Portability and Accountability Act legislation
, 23

Health sector

BD definitions in
, 18

BD needs in
, 18–19

Health-threat detection
, 22

Healthcare
, 14, 139, 155

AI in
, 207

analytics environment
, 19

applications of BD in
, 208

big data applications in
, 4–15

data mining in
, 32–33

IoT in
, 208

ML in
, 207

predictive analytics in
, 10–11

technology in addressing problem of integration
, 208–209

Healthcare electronic record (HER)
, 116

Healthy diet with balanced nutrients
, 223

HEPA filter
, 100–101

High blood pressure
, 227

HIT for Economical and Clinical Health (HITECH)
, 117

Hold out method
, 191

Hospitalization
, 276

House Brackman grading system
, 176

Hyperbolic tangent activation function
, 45

Hyperplanes
, 36

ID3 algorithm
, 158

IECM algorithm
, 183

Image

acquisition
, 182

conversion to arrays
, 105

processing
, 175

segmentation
, 182–183

Information gain
, 37–38

Information gain ratio (IGR)
, 191

Information technology (IT)
, 15, 125

Infrared sensor (IR sensor)
, 142, 144

Infrastructure Plus Program
, 119

Instance-based KNN
, 34

Institute for Health Technology Transformation (IHTT)
, 117, 120

Insulin consumption
, 213–216

Insurance
, 14

Intensive care unit (ICU)
, 124

Intergovernmental Panel Climate Change (IPCC)
, 95

International development
, 13

Internet of Things (IoT)
, 14–15, 19–20, 57, 94, 208

Intrusion detection and security system (IDS system)
, 141

future scope
, 148–149

hardware assembly and implementation
, 144–147

literature review
, 140–141

system architecture
, 141–144

working
, 148

Jaccard distance
, 34

Jaql
, 21

K-most similar data points
, 34

k-Nearest Neighbours (KNN)
, 33–34, 233–234, 267, 269–270

Knowledge-based economy
, 57

Lab testing
, 78

Lazy learning KNN
, 34

Leaky rectified linear unit activation function (Leaky ReLU)
, 45–46

Learning

multiple classifier systems
, 48

phase
, 267

Light-dependent resistor (LDR)
, 141

module
, 142–143, 146

sensor
, 94, 97–98

Linear activation function
, 43

Linear regression
, 40–41, 272–273

Lipomics
, 78

LitmusDx
, 251

LM35 gadget
, 101

Logistic regression
, 40–41, 273–274

Machine learning (ML)
, 21, 32, 42, 66, 190, 207, 224, 233–234, 265

classifiers
, 193–194

Macronutrients
, 226

Magnetic resonance imaging (MRI)
, 116, 174

Mahalanobis distance
, 34

Mahout
, 21

Majority voting
, 50

Malnutrition
, 230–231

Mammography
, 116

Man to machine interaction (M2M interaction)
, 120

Manhattan distance
, 33–34

Manufacturing
, 14

MapReduce
, 21, 84

Margin in SVM
, 36

Max pooling
, 236–237

Meal classification and assessment of nutrients

autumn eats
, 229–230

carbohydrates
, 231

deep learning
, 234–238

fat deficiency
, 231–232

food security
, 230

future scope
, 239–240

life-threatening diseases caused by unhealthy food
, 227

machine learning
, 233–234

malnutrition
, 230–231

mineral deficiency
, 233

problem identification
, 230

proposed solution
, 238–239

protein deficiency
, 232

role and value of nutrients in food
, 224–227

spring eats
, 229

summer eats
, 228

vitamin deficiency
, 232

effect of weather on food
, 228

winter eats
, 229

Mean decrease in impurity (MDI) (see Gini importance)

Media
, 14

Median
, 195–196

filtering
, 183

Medical

claims
, 79–80

imaging
, 3

Medical Termination of Pregnancy (MTP)
, 64

Medicines
, 231

history of
, 244

Medongo
, 251

Memory analytics
, 66

Meta learner
, 50

Metal oxide-semiconductor (MOS)
, 142

Microcontroller
, 141–142

Micronutrients
, 226

MiCS-2714 Gas Sensor
, 100

Migraine
, 204–205

Mineral(s)
, 227

deficiency
, 233

Minkowski distance
, 34

Model

evaluation
, 50–52

selection
, 50

Moisture sensor
, 94, 98

Morphological processing
, 183

Mosaic plot
, 159, 167

MQ2 Sensor
, 99–100, 142, 145

MQ9 Sensor
, 100

Multiple voxel pattern analysis (MVPA)
, 209

Multiway frequency analysis
, 274

Mutation
, 244

“My Kardio” framework
, 256

Naïve Bayes (NB)
, 190, 194

algorithm
, 34–35

classification modeling
, 270–272

classifier
, 34–35

National Family Health Survey (NFHS)
, 63

National Health Service (NHS)
, 127

National Institute of Health (NIH)
, 119

NCI tool
, 10

Nerve transfer
, 178

Neural networks
, 21, 42–43

activation functions
, 43

hyperbolic tangent activation function
, 45

Leaky ReLU
, 45–46

linear activation function
, 43

ReLU function
, 45

sigmoid activation function
, 43

Softmax activation function
, 46–47

Neurosynaptic communications
, 249–250

Neurosynaptic Communications Private Ltd (NCPL)
, 249

Noise removal methods
, 105

Non-essential nutrients
, 227

Non-parametric KNN
, 34

Non-suicidal trauma factor (NSSI)
, 209

Normal AC Filter
, 101

Numeric predictions
, 36

Nutrients
, 223–224

role and value of nutrients in food
, 224–227

Obesity
, 205, 227

Object-oriented programs
, 56

Omics
, 78–79

Oozie
, 21

Optical imaging
, 116

Optimized delivery in telehealth care
, 252

Osteoporosis
, 227

Palsy
, 173

Partial facial paralysis
, 174

Patient

engagement
, 8–9

health record
, 116

patient-centric care
, 22

predication
, 3

Patient disease (Pdis)
, 161

Pattern recognition technique
, 21

Peripheral facial palsy
, 174

Personalized healthcare
, 77

pH sensor
, 94, 98

Photograph objects
, 183

Photoresistor
, 97–98

Photosynthesis
, 96

PIG Latin
, 84

PlantVillage
, 104

Plurality voting
, 50

PM 2.5 Sensor
, 100

Population health
, 22

Positron emission tomography (PET)
, 116

Potentiometric pH meter
, 98

Power Grid Data
, 4

PPD42NJ Particle Sensor Unit
, 99

Precision
, 51

Prediabetes
, 204

Predictive analytics
, 56, 67, 220

in health care
, 10–11

Predictive big data analytics in healthcare (see also Big data analytics (BDA))
, 76

advantages
, 84–85

areas of application
, 81–84

challenges
, 86–88

claims data
, 79–80

clinical data
, 77–79

clinical research data
, 80

data-related concerns
, 87

infrastructural concerns
, 86

IT infrastructure benefits
, 84

managerial benefits
, 85

operational benefits
, 84–85

organization-related concerns
, 88

organizational benefits
, 85

patient–generated data
, 80–81

security/privacy concerns
, 87–88

sources of big data in healthcare
, 77–81

strategic benefits
, 85

Predictive healthcare
, 77

Predictive modeling in health care data analytics
, 264–266

applications
, 274–277

disease diagnosis and treatment selection
, 274–276

health care management
, 276–277

reducing health care costs
, 277

techniques for
, 267–274

Prescription claims
, 79–80

Prescriptive analytics
, 56, 67

Preservatives
, 227

Pressure sensor
, 102

“Process of transformation”
, 60

Protein(s)
, 226

deficiency
, 232

Proteomics
, 78

Pruning
, 39

PS2 Pollen Sensor
, 99

Public health
, 22

Radionuclide imaging
, 116

Random decision forest
, 193

Random forest (RF)
, 190–191, 193, 275

algorithm
, 39–40

classifier
, 40

experimental results
, 197–200

machine learning classifiers
, 193–194

proposed methodology
, 196–197

statistical analysis
, 194–196

Raspberry Pi
, 141

Recall
, 51–52

Receiver Operating Characteristics curve (ROC curve)
, 52

Recommender system
, 131

Rectified linear unit activation function (ReLU function)
, 45, 236

Recurrent Neural Networks (RNN)
, 47, 237–238

Relay
, 143–144

ReMeDi Nova
, 250

ReMeDi Platform
, 250

ReMeDi Solution
, 249–250

Research and development professionals (R&D professionals)
, 56

Resizing images
, 105

Right to Information Act (2005)
, 71

Risk-scoring
, 79

Root mean square error (RMSE)
, 191, 196

Root relative squared error (RRSE)
, 191

Search Engine Data
, 4

Second-level learner (see Weak learners)

Security
, 11, 139–140

Semi-supervised learning
, 193

Sensor(s)
, 140

data
, 19–20

Shannon’s entropy
, 37

Sigmoid activation function
, 43

Sigmoid function
, 41

Simple averaging
, 49

Simple carbohydrates
, 226

Smart nursery with health monitoring system

data acquiring and preprocessing
, 104–105

data modeling
, 105–106

literature survey
, 95–103

methodology
, 103–106

results
, 110–112

solution
, 106–110

Smartwatches (see Wristwatches)

Social media data
, 80

Soft voting
, 50

Softmax activation function
, 46–47

Software for Flexible Integration of Annotation (SoFIA)
, 131

Soil pressure
, 102

Spatial analysis
, 21

Spring eats
, 229

Sqoop
, 21

Stacking
, 50

Staffing levels
, 5

Standard deviation (SD)
, 196

Statistical analysis
, 194–196

Stress
, 204

Sulfur dioxide
, 96

Summer eats
, 228

Supervised learning
, 37, 193, 265

Support vector machines (SVM)
, 35–37, 190, 192–194, 234, 256, 267

advantages and disadvantages
, 234

Support vectors (SV)
, 234

Synkinesis
, 177

Tanimoto distance
, 34

Telecardiology
, 248

Teledermatology
, 248

Telehealth
, 243, 246–247

barriers to
, 252–257

business models
, 249–252

early civilization
, 245

evolution
, 246

history of medicine
, 244

methodology
, 257

modern history
, 245–246

optimized delivery in telehealth care
, 252

pre-historic ERA
, 244–245

process of evolution
, 244

results
, 257–261

Telemedicine
, 12–13, 77, 247–249

Telenephrology
, 248

Teleneurology
, 248

Teleobstetrics
, 249

Teleoncology
, 249

Teleophthalmology
, 248

Telepathology
, 249

Telepsychiatry
, 248

Telerehabilitation
, 249

Temperature sensor
, 101

Tension-type headaches (TTH)
, 205, 215

Text mining
, 66

medical records
, 11

Thermography
, 116

Tin dioxide (SnO2)
, 142

True negative (TN)
, 51

True positive (TP)
, 51

Type 1 diabetes (T1D)
, 204, 204, 216

Ultrasonography (US)
, 116

Undernourishment
, 231

Unsupervised learning
, 193, 265

Valance
, 125

Validity
, 126

Value
, 126

Variance
, 196

Variety
, 4, 18, 124

Velocity
, 4, 18, 124

Veracity
, 125

Visualization
, 126

Vitamin(s)
, 226–227

deficiency
, 232

Volatility
, 126

Volume
, 4, 18, 123

Voting
, 50

Vulnerability
, 126

Water
, 226

water-soluble vitamins
, 227

Weak learners
, 48

Wearable sensors
, 81

Weighted averaging
, 50

Weighted voting
, 50

Whole-genome association study (WGAS)
, 131

Winter eats
, 229

Wireless sensor network (WSN)
, 141

Wisconsin Breast Cancer Data (WBCD)
, 190

World Health Organization (WHO)
, 122, 275

Wristwatches
, 116

X-ray

computed tomography
, 116

radiography
, 116

YOLO Health
, 251

Zookeeper
, 21, 84