AI and Blockchain in Healthcare

Preface
Contents
Part I Role of AI and Blockchain in Healthcare
1 Machine Learning for Drug Discovery and Manufacturing
1.1 Introduction
1.2 Machine learning (ML)
1.2.1 Artificial Intelligence (AI)
1.2.2 Machine Learning Techniques (MLTs)
1.2.3 Classification of ML
1.3 ML for Drug Discovery and Manufacturing
1.3.1 ML and Drug Discovery
1.3.2 MLTs Role in Drug Discovery
1.3.3 Examples of MLTs in Drug Discovery
1.3.4 Support Vector Machine
1.3.5 Random Forest
1.3.6 Multilayer Perceptron
1.3.7 Deep Learning
1.4 ML Applications in Drug Production
1.5 Challenges and Risks
1.6 Conclusions and Future Perspectives
References
2 Knowledge Strategies Influencing on the Epidemiologists Performance of the Qeshm Island’s Health Centers
2.1 Introduction
2.2 Literature Review
2.3 Qeshm Island
2.4 Research Methodology
2.4.1 Analytical Network Process (ANP)
2.4.2 ANP-Proposed Algorithm
2.5 Results and Discussion
2.6 Conclusion
References
3 Healthcare: In the Era of Blockchain
3.1 Introduction
3.2 Related Work
3.3 Application Areas of Blockchain in Healthcare
3.4 Limitations and Challenges in the Adoption of Blockchain in Healthcare
3.5 Conclusion
References
4 Securing Healthcare Records Using Blockchain: Applications and Challenges
4.1 Introduction
4.2 Why Blockchain?
4.3 Applications
4.4 Limitations
References
5 Authentication Schemes for Healthcare Data Using Emerging Computing Technologies
5.1 Introduction
5.2 Related Work
5.3 Text Analytics of Related Studies Using Word Cloud
5.3.1 Proposed Method
5.3.2 Data Collection
5.3.3 Data Analysis
5.3.4 Discussion
5.4 Conclusion
References
6 Biomedical Data Classification Using Fuzzy Clustering
6.1 Introduction
6.2 Fuzzy Logic and Biomedical Data
6.3 Need for Fuzzy Logic in Biomedical Domain
6.4 Various Types of Clustering on Biomedical Data
6.4.1 Fuzzy c-Means Algorithm (FCM)
6.4.2 Hierarchical Clustering
6.4.3 K-means Clustering Algorithm
6.5 Conclusion
References
Part II Application of AI and Blockchain in Healthcare
7 Applications of Machine Learning in Healthcare with a Case Study of Lung Cancer Diagnosis Through Deep Learning Approach
7.1 Introduction
7.2 Related Work
7.3 Background
7.3.1 Convolutional Neural Network
7.3.2 Deep Learning
7.3.3 Applications of Machine Learning
7.3.4 Lung Cancer Causes
7.4 Conclusion
References
8 Fetal Health Status Prediction During Labor and Delivery Based on Cardiotocogram Data Using Machine and Deep Learning
8.1 Introduction
8.2 Related Work
8.3 Methodology
8.3.1 Machine Learning Models
8.3.2 Deep Learning Models
8.4 Experimental Evaluation and Result Discussion
8.4.1 Brief Dataset Description
8.4.2 Performance Metrics
8.4.3 Data Visualization and Data Pre-processing
8.4.4 Performance Evaluation—Machine Learning Models
8.4.5 Performance Evaluation—Deep Learning Models
8.5 Conclusion
References
9 Blockchain and AI: Disruptive Digital Technologies in Designing the Potential Growth of Healthcare Industries
9.1 Introduction
9.2 Review of AI in Health Care
9.3 Applications of AI in Health Care
9.4 Review of Blockchain and AI in Health Care
9.5 Blockchain Framework
9.6 Applications of Blockchain in Health Care
9.6.1 Health Records
9.6.2 Supply Chains
9.6.3 Genomic Market
9.7 Metaverse
9.8 Metaverse for Health and Wellbeing
9.9 Limoverse, The Blockchain and AI Revolution in Health Care
9.10 Impact of Blockchain and AI in Health Care
9.11 Future Prospects of Blockchain and AI in the Healthcare Ecosystem
9.12 Conclusion
References
10 Recommendation Systems for Cancer Prognosis, Treatment and Wellness
10.1 Introduction
10.2 Cancer Diagnoses, Treatment and Rehabilitation
10.3 Applications of Computer Based System in Cancer Study
10.4 Recommendation Systems: History and Introduction
10.5 Recommendation SystemsAlgorithms for Cancer Study
10.5.1 Predicting Cancer Drug Response Using a Recommender System
10.5.2 Recommender System for Breast Cancer Patients
10.5.3 Personal Health Information Recommender for Empowering Cancer Patients
10.5.4 Gene Based Recommendation Algorithm to Recommend Genes for Cancer Patients
10.6 Recommendation System with Blended Approach for Breast Cancer Diagnosis-BC Recommender
10.6.1 Methodology
10.7 Observations and Discussion
10.8 Challenges and Future Work
References
11 Real-Time Data Mining-Based Cancer Disease Classification Using KEGG Gene Dataset
11.1 Introduction
11.2 Literature Survey
11.3 CFARM-KEGG Architecture
11.4 Results
11.5 Conclusion
References
12 Solution Architecting on Remote Medical Monitoring with AWS Cloud and IoT
12.1 Introduction
12.2 Literature Review
12.3 Internet of Things (IoT)
12.4 Cloud Healthcare Management
12.4.1 Infrastructure as a Service (IaaS)
12.4.2 Platform as a Service (PaaS)
12.4.3 Software as a Service (SaaS)
12.4.4 Deployment Models
12.4.5 Cloud
12.4.6 Hybrid
12.4.7 On-Premises
12.5 AWS
12.6 Conclusion
References
13 A Domain Oriented Framework for Prediction of Diabetes Disease and Classification of Diet Using Machine Learning Techniques
13.1 Introduction
13.1.1 Machine Learning
13.2 Literature Survey
13.3 Framework for Diabetic Prediction and Diet Using Machine Learning
13.3.1 Data Set Collection
13.3.2 Data Preprocessing
13.3.3 Data Distribution
13.3.4 Data Exploring and Cleaning
13.4 Machine Learning Classification
13.5 Algorithms for Food Recommendation to Diabetic Patients by Using Machine Learning
13.5.1 Diet Recommendations for Diabetic Patients
13.5.2 Characteristics of Diabetes
13.5.3 Fruit Consumption and Diabetes Prevention
13.5.4 How Does Fruit Consumption Help to Avoid Diabetes
13.6 Experimental Setup
13.7 Result and Analysis
13.8 Conclusion and Future Work
References
14 An Accurate Swine Flu Prediction and Early Prediction Using Data Mining Technique
14.1 Introduction
14.2 Literature Survey
14.3 Existing Methods
14.4 Conclusion
14.5 Future Work
References