Data Science for Effective Healthcare Systems

✍ Scribed by Hari Singh, Ravindara Bhatt, Prateek Thakral, Dinesh Chander Verma

• Publisher: CRC Press/Chapman & Hall
• Year: 2022
• Tongue: English
• Leaves: 225
• Category: Library

✦ Synopsis

Data Science for Effective Healthcare Systems has a prime focus on the importance of data science in the healthcare domain. Various applications of data science in the health care domain have been studied to find possible solutions. In this period of COVID-19 pandemic data science and allied areas plays a vital role to deal with various aspect of health care. Image processing, detection & prevention from COVID-19 virus, drug discovery, early prediction, and prevention of diseases are some thrust areas where data science has proven to be indispensable.

Key Features

The book offers comprehensive coverage of the most essential topics, including

Big Data Analytics, Applications & Challenges in Healthcare
Descriptive, Predictive and Prescriptive Analytics in Healthcare
Artificial Intelligence, Machine Learning, Deep Learning and IoT in Healthcare
Data Science in Covid-19, Diabetes, Coronary Heart Diseases, Breast Cancer, Brain Tumor
The aim of this book is also to provide the future scope of these technologies in the health care domain. Last but not the least, this book will surely benefit research scholar, persons associated with healthcare, faculty, research organizations, and students to get insights into these emerging technologies in the healthcare domain.

✦ Table of Contents

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
Editor
1. Big Data in Healthcare: Applications and Challenges
1.1 Introduction
1.2 Types of Data in Healthcare
1.2.1 Business, Organizational, and External Data
1.2.2 Patient Sentiment and Behavior Data
1.2.3 Clinical Information and Notes
1.2.4 Web-and Social Networking–Based Data
1.2.5 Genomic Data
1.3 Big Data 5 V's in Healthcare
1.3.1 Volume
1.3.2 Velocity
1.3.3 Veracity
1.3.4 Variety
1.3.5 Value
1.4 Big Data Analysis in Healthcare Industry
1.4.1 Data Acquisition
1.4.2 Data Storage
1.4.3 Data Management
1.4.4 Data Analytics
1.4.5 Data Visualization
1.5 Big Data Analytics Tools in Healthcare
1.6 Applications of Big Data in Healthcare
1.6.1 Data Analytics in COVID-19
1.6.2 Hadoop-Based Applications
1.6.3 Big Data in Public Health and Behavior Research
1.6.4 Source of Valuable Data
1.6.5 Big Data in Medical Experiment
1.6.6 Medical Research Using Big Data
1.7 Challenges with Healthcare Data Management
1.7.1 Challenges Associated with Manpower
1.7.2 Challenges in Data and Process
1.7.3 Overall Organizational Challenges
1.8 Conclusion
References
2. Impact Analysis of COVID-19 on Different Countries: A Big Data Approach
2.1 Introduction
2.2 Processing Steps of Big Data
2.2.1 Data Collection and Recording
2.2.2 Data Cleaning
2.2.3 Data Integration
2.2.4 Data Modeling
2.2.5 Data Interpretation
2.3 Challenges of Big Data Analysis
2.3.1 Heterogeneity and Incompleteness
2.3.2 Scalability
2.3.3 Timeliness
2.3.4 Analytics of Big Data
2.4 Current Scenario in Top Five Countries Affected by Pandemic
2.5 Process Adopted to Carry Out the Analysis
2.6 Major Factors that Can Majorly Affect the Result of Analysis
2.7 Conclusion
References
3. Overview of Image Processing Technology in Healthcare Systems
3.1 Introduction
3.2 Computer-Based Technology
3.3 Image Recognition, Analysis, and Enhancements
3.4 Role of Machine Learning and DL in the Field of Medical Diagnosis
3.5 Development in Remote Healthcare with Mobile Phone and Telemedicine Systems
3.6 Applications in Health Research
3.7 Conclusion
References
4. Artificial Intelligence to Fight against COVID-19 Coronavirus in Bharat
4.1 Introduction
4.2 Viral Gene Sequencing Based on AI
4.3 Diagnosis of New Coronary Pneumonia Based on Machine Vision
4.4 New Coronary Drug Screening Based on AI+ Big Data
4.5 Using Computer Vision to Detect Coronavirus Infection
4.6 AI in Other Areas
4.7 Conclusions
References
5. Classification-Based Prediction Techniques Using ML: A Perspective for Health Care
5.1 Introduction
5.2 Related Work
5.3 Unlocking the Power of Classification and Prediction Techniques using ML in Health Care
5.3.1 Machine Learning
5.3.2 ML in Health Care
5.4 ML in Disease Prediction and Detection
5.4.1 ML in Diabetes Prediction
5.4.2 ML in Cancer Prediction
5.4.3 ML in Heart Disease Prediction
5.5 Applications of Classification in Health Care
5.6 Challenges and Opportunities for ML in Health Care
5.6.1 Challenges in Health Care System
5.6.2 Unlocking the Opportunities of ML in Health Care
5.7 Conclusion
References
6. Deep Learning for Drug Discovery: Challenges and Opportunities
6.1 Introduction
6.2 Principles of DL
6.3 DL Methods for Drug Discovery
6.3.1 DNN
6.3.2 CNN
6.3.3 RNN
6.3.4 AEs
6.3.5 DBN
6.4 Opportunities and Challenges
6.4.1 Drug Safety
6.4.2 Integration of Biomedical Information with Computational Methodologies
6.4.3 Genetically Analysis of Data and Customized Medication
6.4.4 Building and Getting Knowledge from Databases
6.4.5 ML Methods for Genetics and Genomics
6.5 Applications of DL in Drug Discovery
6.5.1 Drug Properties Prediction
6.5.2 De Novo Drug Design
6.5.3 Drug–Target Interaction Prediction
6.6 Conclusion
References
7. Issues and Challenges Associated with Machine Learning Tools for Health Care System
7.1 Introduction
7.2 Machine-Learning–Based Prediction Schemes for Healthcare Industry
7.3 Automated Decision Support System
7.4 Drug Discovery and Human Trials using Machine Learning
7.5 Surgical Operations with Machine Learning Assistance
7.6 Conclusion
References
8. Real-Time Data Analysis of COVID-19 Vaccination Progress Over the World
8.1 Introduction
8.2 Literature Review
8.3 Methodology
8.3.1 Data Collection
8.3.2 Data Preprocessing
8.3.3 Feature Engineering
8.4 Data Analysis
8.4.1 World Data Analysis
8.4.2 Vaccines Used by Different Countries
8.4.3 Vaccination of World's Top 30 Countries
8.4.4 Top Ten Vaccinated Countries' Vaccination Information
8.4.5 Top Vaccinated Countries and Vaccines Used
8.4.6 Daily Vaccination in Bangladesh
8.5 Conclusions
References
9. Descriptive, Predictive, and Prescriptive Analytics in Healthcare
9.1 Introduction
9.2 Descriptive Analytics
9.3 Predictive Analytics
9.4 Prescriptive Analytics
9.5 Analytics Techniques in Healthcare
9.5.1 Supervised Learning
9.5.1.1 Classification
9.5.1.2 Regression
9.5.2 Unsupervised Learning
9.5.2.1 Clustering
9.5.2.2 Dimension Reduction
9.6 Healthcare Analytics Life Cycle
9.7 Proposed Architecture for Healthcare Analytics
9.8 Conclusion
References
10. IoT Enabled Worker Health, Safety Monitoring and Visual Data Analytics
10.1 Introduction
10.2 Connected Assets
10.2.1 Connected People
10.2.2 Connected Vehicles
10.3 Protection of the Environment and Conservation of Water
10.4 Mine Planning
10.5 Proposed Connected Mining Solution
10.5.1 Application and Visualization
10.5.2 Core Platform and Services
10.5.3 IoT Platform and Services
10.5.4 Communication Network and Protocols
10.5.5 Devices and Connectivity Characteristics
10.6 Development and Deployment
10.6.1 Application Dashboard
10.6.2 Intrusion Detection and Worker's Health Dashboard
10.7 Conclusion
References
11. Prevalence of Nomophobia and Its Association with Text Neck Syndrome and Insomnia in Young Adults during COVID-19
11.1 Introduction
11.2 Aim of the Study
11.3 Hypotheses
11.4 Review of Literature: Nomophobia
11.5 Review of Literature: TNS
11.6 Review of Literature: Insomnia
11.7 Methodology
11.8 Result
11.8.1 Part I: Demographic Characteristics of Subjects
11.8.2 Part II: Hypothesis Testing
11.8.2.1 TNS v/s Nomophobia
11.8.2.2 AIS v/s Nomophobia
11.8.2.3 AIS v/s TNS
11.8.3 Part III: Odds Ratio and Relative Risk Ratio Analysis
11.8.3.1 AIS v/s Nomophobia
11.8.3.2 AIS v/s Nomophobia
11.8.3.3 AIS v/s TNS
11.9 Discussion
11.10 Conclusion
11.11 Future Scope
References
12. The Role of AI, Fuzzy Logic System in Computational Biology and Bioinformatics
12.1 Introduction
12.1.1 Bioinformatics
12.1.2 Challenges in Bioinformatics
12.2 Computational Biology and Bioinformatics: A Comparison
12.3 Machine Learning Approach
12.4 Artificial Neural Network Approach
12.5 BLAST Algorithm
12.6 Advancement of Deep Learning Architectures in Bioinformatics
12.7 Fuzzy Logic in Bioinformatics
12.8 Bioinformatics in COVID-19
12.9 Fuzzy Rough Set Theory on Cancer Diagnosis
12.9.1 Dataset Description
12.9.2 Result and Analysis
12.10 Conclusion with Future Opportunity
References
13. Analysis for Early Prediction of Diabetes in Healthcare Using Classification Techniques
13.1 Introduction
13.2 Literature Survey
13.3 Materials and Methods
13.3.1 Data Preprocessing
13.4 ML Techniques
13.4.1 SVM
13.4.2 KNN
13.4.3 Logistic Regression
13.4.4 Ensemble Method (Random Forest)
13.4.5 Gaussian Naive Bayes
13.5 Results and Discussion
13.6 Conclusion
References
14. Nomenclature of Machine Learning Algorithms and Their Applications
14.1 Introduction
14.2 Related Work
14.3 A Brief Overview of the Five ML Algorithms
14.3.1 Support Vector Machine (SVM)
14.3.2 KNN
14.3.3 NB
14.3.4 DT
14.3.5 Logistic Regression
14.4 Proposed Methodology
14.4.1 Performance Evaluation Metrics
14.5 Results and Discussion
14.6 Conclusions
References
15. Breast Cancer Prognosis Using Machine Learning Approaches
15.1 Introduction
15.1.1 Breast Cancer
15.2 Role of Machine Learning
15.2.1 ML Methods
15.2.2 ML Algorithms for Breast Cancer Prognosis
15.2.2.1 Naïve Bayes Classifier
15.2.2.2 Decision Trees
15.2.2.3 Support Vector Machine
15.2.2.4 KNN
15.2.2.5 K-Means
15.2.2.6 Random Forest
15.2.2.7 Logistic Regression
15.2.2.8 ANN
15.2.3 ML Approach for Breast Cancer Prognosis
15.3 Experimental Summary
15.4 Challenges
15.5 Conclusion
References
16. Machine Learning-Based Active Contour Approach for the Recognition of Brain Tumor Progression
16.1 Introduction
16.2 Literature Survey
16.2.1 Solutions for Automated Diagnosis
16.2.2 Classification-Based solutions
16.2.3 SVM-Based solutions
16.2.4 CNN-Based solutions
16.2.5 Active Contour–Based Solutions
16.2.6 Feature-Extraction–Based Solutions
16.3 ML-Based Active Contour Approach
16.4 Simulation and Performance Analysis
16.5 Conclusion
References
17. A Deep Neural Networks-Based Cost-Effective Framework for Diabetic Retinopathy Detection
17.1 Introduction
17.2 Related Work
17.3 Methodology
17.3.1 Proposed Computational CNN
17.3.1.1 Preprocessing of Dataset
17.3.1.2 Functionality of Proposed Convolutional Network
17.3.2 Framework User Interface
17.4 Results and Discussion
17.5 Conclusion
References
Index