Data Science in the Medical Field

Chapter 1: PPH 4.0: a privacy-preserving health 4.0 framework with machine learning and cellular automata

• • 1.1 Introduction
• • 1.2 A brief survey of related technologies and past important works
• • 1.2.1 Machine learning
• • 1.2.2 MapReduce
• • 1.2.3 Attribute-based encryption
• • 1.2.4 Cellular automata
• • 1.2.5 Elementary cellular automata alignment
• • 1.2.6 Elementary cellular automata rules
• • 1.3 Research methodology and proposed framework
• • 1.3.1 Supervised approach for data dimensionality reduction
• • 1.3.2 Filter methods
• • 1.3.3 Correlation coefficient
• • 1.3.4 Information gain
• • 1.3.5 Fisher score
• • 1.3.6 Mutual information
• • 1.3.7 Chi-square
• • 1.3.8 Wrapper methods
• • 1.3.9 Forward selection approach
• • 1.3.10 Backward elimination approach
• • 1.3.11 Embedded methods
• • 1.3.12 Feature transformation
• • 1.3.13 Linear discriminant analysis
• • 1.3.14 Principal component analysis
• • 1.3.15 Autoencoder
• • 1.3.16 Metric learning
• • 1.4 Discussions
• • 1.4.1 Integration of methods and disciplines to pursue the objectives
• • 1.5 Conclusions and future research direction
• • Acknowledgment
• • Declaration of competing interest
• • References

Chapter 2: An automatic detection and severity levels of COVID-19 using convolutional neural network models

• • 2.1 Introduction
• • 2.2 Related work
• • 2.3 Early diagnosis by deep learning strategies
• • 2.4 Methodology
• • 2.4.1 Preprocessing
• • 2.4.2 Data augmentation
• • 2.4.3 Transfer learning
• • 2.4.4 Segmentation using the U-Net architecture
• • 2.4.5 Classification using ReseNet50 or VGG16
• • 2.5 Dataset and implementation
• • 2.6 Performance evaluation metrics
• • 2.6.1 Accuracy
• • 2.6.2 Sensitivity
• • 2.6.3 Precision
• • 2.6.4 F-score
• • 2.7 Comparison
• • 2.8 Conclusion
• • References

Chapter 3: Biosensors and disease diagnostics in medical field

• • 3.1 Introduction
• • 3.1.1 Importance of disease diagnostics in healthcare
• • 3.1.2 Role of biosensors in disease diagnosis
• • 3.2 Principle of biosensor
• • 3.3 Architecture of biosensor structure
• • 3.4 Different types of medical sensors
• • 3.5 Biosensor technologies in disease diagnosis
• • 3.6 Application of biosensors
• • 3.6.1 Infectious diseases
• • 3.6.2 Cancer
• • 3.6.3 Diabetes
• • 3.6.4 Cardiovascular diseases
• • 3.6.5 Neurological disorders
• • 3.6.6 Autoimmune diseases
• • 3.7 Challenges and limitations of biosensors in disease diagnostics
• • 3.8 Future perspectives and advancements in biosensor technology
• • 3.8.1 Integration of biosensor with artificial intelligence and machine learning
• • 3.8.2 Wearable biosensors and remote monitoring
• • 3.9 Commercial and clinical adoption of biosensors
• • 3.9.1 Current market landscape of biosensors in the medical field
• • 3.9.2 Challenges in commercialization and widespread adoption
• • 3.10 Case studies of biosensor applications
• • 3.11 Conclusion
• • 3.11.1 Future implications of biosensors in healthcare and medical field
• • References

Chapter 4: Brain tumor recognition and classification techniques

• • 4.1 Introduction
• • 4.2 Imaging methods
• • 4.3 Brain tumor detection, segmentation, and classification
• • 4.3.1 Brain tumors detection and segmentation
• • 4.4 Conclusion and discussion for segmentation
• • 4.4.1 Brain tumor classification
• • 4.5 Analysis study
• • 4.6 Conclusion and discussion for classification
• • 4.7 Conclusion and future prospects
• • References

Chapter 5: Identifying the features and attributes of various artificial intelligence-based healthcare models

• • 5.1 Introduction
• • 5.2 Predictive analytics models
• • 5.2.1 IBM Watson Health
• • 5.2.2 Google DeepMind Health
• • 5.2.3 Ayasdi
• • 5.2.4 Cerner
• • 5.2.5 Epic systems
• • 5.2.6 Statistical Analysis System
• • 5.3 Natural Language Processing models
• • 5.3.1 BioBERT
• • 5.3.2 ClinicalBERT
• • 5.3.3 MedBERT
• • 5.3.4 BlueBERT
• • 5.3.5 PubmedBERT
• • 5.3.6 Clinical BERT-based question answering
• • 5.3.7 MedCAT
• • 5.3.8 i2b2 Natural Language Processing framework
• • 5.4 Chatbot models
• • 5.4.1 Microsoft healthcare bot
• • 5.4.2 Woebot
• • 5.4.3 Your.MD
• • 5.4.4 Buoy health
• • 5.4.5 Babylon health
• • 5.4.6 Infermedica
• • 5.4.7 Ada Health
• • 5.5 Computer Vision models
• • 5.5.1 Mammography computer-aided detection systems
• • 5.5.2 Retinal fundus imaging
• • 5.5.3 Histopathology image analysis
• • 5.5.4 Skin lesion analysis
• • 5.5.5 Analysis of radiology imaging
• • 5.5.6 Surgical vision systems
• • 5.6 Conclusion
• • 5.7 Artificial intelligence disclosure
• • References

Chapter 6: Classification algorithms and optimization techniques in healthcare systems representation of dataset in medical applications

• • 6.1 Introduction
• • 6.2 Related works
• • 6.3 Types of classification
• • 6.4 Architecture and methods
• • 6.5 Proposed attributes and methods
• • 6.6 Efficiency and performance
• • 6.7 Experiential result discussion
• • 6.8 Conclusion
• • References

Chapter 7: A knowledge discovery framework for COVID-19 disease from PubMed abstract using association rule hypergraph

• • 7.1 Introduction
• • 7.2 Related works
• • 7.3 Methodology
• • 7.3.1 Data gathering and preprocessing
• • 7.3.2 Keyword extraction using latent Dirichlet allocation with affinity propagation clustering
• • 7.3.3 Generation of the association using affinity propagation-hypergraph
• • 7.4 Experimental analysis
• • 7.4.1 Data gathering and preprocessing
• • 7.4.2 TF-IDF estimation
• • 7.4.3 Latent Dirichlet allocation with affinity propagation
• • 7.4.4 Extracted association
• • 7.4.5 Comparative analysis
• • 7.5 Conclusion
• • Author contribution
• • Acknowledgment
• • References

Chapter 8: Predictive analysis in healthcare using data science: leveraging big data for improved patient care

• • 8.1 Introduction
• • 8.2 Data science in healthcare: an overview
• • 8.2.1 Role of data science in healthcare transformation
• • 8.2.2 Healthcare data science: progress challenges and opportunities
• • 8.3 Predictive analysis techniques in healthcare
• • 8.3.1 Data collection and reprocessing
• • 8.3.2 Feature selection and engineering
• • 8.3.3 Predictive analysis using machine learning models
• • 8.3.4 Predictive model evaluation and validation
• • 8.4 Application of predictive analysis in healthcare
• • 8.4.1 Early disease detection and diagnosis
• • 8.4.2 Personalized treatment planning
• • 8.4.3 Hospital resource management and patient flow optimization
• • 8.4.4 Public health surveillance and outbreak prediction
• • 8.5 Case studies and success stories
• • 8.5.1 Case study: IBM Watson health and chronic disease management
• • 8.5.2 Case study: Pfizer’s predictive analytics for adverse drug reactions
• • 8.5.3 Case study: partners healthcare and hospital readmission reduction
• • 8.6 Conclusion and future research direction
• • References

Chapter 9: Data science in medical field: advantages, challenges, and opportunities

• • 9.1 Introduction
• • 9.2 Literature review
• • 9.3 Overview of data science in medical field
• • 9.4 Applications of data science in medical field
• • 9.4.1 Predictive analytics
• • 9.4.2 Diagnostics tools
• • 9.4.3 Pharmaceutical services
• • 9.4.4 Drug discovery and development
• • 9.4.5 Healthcare resource optimization
• • 9.4.6 Disease surveillance and outbreak prediction
• • 9.4.7 Continuous monitoring and remote patient care
• • 9.5 Advantages of data science in healthcare sector
• • 9.6 Challenges of data science in healthcare sector
• • 9.6.1 Data management
• • 9.6.2 Privacy and security
• • 9.6.3 Data retention
• • 9.6.4 Maintaining cybersecurity
• • 9.7 Opportunities of data science in healthcare sector
• • 9.8 Discussion and future directions
• • 9.9 Conclusion
• • Further reading

Chapter 10: Decentralizing healthcare through parallel blockchain architecture: transmitting internet of medical things data through smart contracts in telecare medical information systems

• • 10.1 Introduction
• • 10.2 Literature review
• • 10.2.1 Telecare medical information systems
• • 10.2.2 Blockchain technology
• • 10.2.3 Internet of medical things
• • 10.2.4 Patientdoctor parallel-chain communication
• • 10.3 Network architecture and implementation
• • 10.3.1 Parallel blockchain architecture
• • 10.3.2 Communication layer
• • 10.4 Application development and smart-contract deployment
• • 10.4.1 Application structure
• • 10.4.2 Smart-contract deployment
• • 10.4.3 User interface/user experience
• • 10.5 Results and discussion
• • 10.6 Conclusion
• • 10.7 Future work
• • References

Chapter 11: Machine learning in heart disease prediction

• • 11.1 Introduction
• • 11.2 Literature review
• • 11.3 Proposed method
• • 11.3.1 Random forest
• • 11.3.2 Supporting vector machine
• • 11.3.3 Artificial neural networks
• • 11.4 Methodology
• • 11.4.1 Data collection
• • 11.4.2 Data exploration
• • 11.4.3 Data set collection
• • 11.4.4 Attribute selection
• • 11.4.5 Data preprocessing
• • 11.4.6 Balancing of data
• • 11.4.7 Disease prediction
• • 11.5 Software requirement
• • 11.5.1 Anaconda
• • 11.5.2 Python
• • 11.5.3 Numpy
• • 11.5.4 Pandas
• • 11.5.5 Sklearn
• • 11.5.6 Tensorflow
• • 11.5.7 Objective and types of testing
• • 11.6 Conclusion
• • References

Chapter 12: U-Net-based approaches for brain tumor segmentation

• • 12.1 Introduction
• • 12.2 Brain tumors
• • 12.3 Magnetic resonance imaging
• • 12.3.1 Radiologists’ role in magnetic resonance imaging image analysis
• • 12.4 Deep learning
• • 12.5 Convolutional neural networks
• • 12.6 U-Net
• • 12.7 Summary of related work
• • 12.7.1 Methodology
• • 12.8 Experimental setup
• • 12.8.1 Process
• • 12.8.2 Data preprocessing
• • 12.9 Model building and training
• • 12.9.1 Data split
• • 12.9.2 Performance evaluation
• • 12.9.3 Implementation and results
• • 12.10 2D U-Net architecture
• • 12.11 2D Modalities results
• • 12.11.1 Optimization algorithm results
• • 12.11.2 Activation function results
• • 12.11.3 Normalization and dropout results
• • 12.12 3D U-Net architecture
• • 12.13 3D modalities results
• • 12.13.1 Normalization and dropout results
• • 12.14 Residual U-Net architecture
• • 12.15 Activation function results
• • 12.16 Normalization and dropout results
• • 12.17 Attention U-Net architecture
• • 12.18 Normalization and dropout results
• • 12.19 Residual attention U-Net architecture
• • 12.20 Normalization and dropout results
• • 12.21 Architecture comparison
• • 12.22 Conclusion
• • 12.23 Research contribution
• • 12.24 Future work
• • References

Chapter 13: Explainable image recognition models for aiding radiologists in clinical decision making

• • 13.1 Introduction
• • 13.2 Literature review
• • 13.3 Proposed work
• • 13.3.1 Data gathering and preparation
• • 13.3.2 Annotation of abnormal regions or abnormalities in the dataset
• • 13.3.3 Preprocessing steps for image enhancement and normalization
• • 13.3.4 Training an abnormality detection model
• • 13.3.5 Abnormality detection and localization
• • 13.4 X-ray
• • 13.5 Magnetic resonance imaging scan
• • 13.5.1 Assessment of the performance of abnormality detection for each scan type
• • 13.5.2 Extraction of abnormality information
• • 13.5.3 Text generation for abnormality narration
• • 13.5.4 Presentation of abnormality narration to the user
• • 13.6 Experimental results
• • 13.6.1 Performance metrics
• • 13.6.2 4.3 Text generation metrics
• • 13.6.3 4.4 Comparison with existing methods
• • 13.7 Concluding remarks and prospects
• • References

Chapter 14: Prediction of heart failure disease using classification algorithms along with performance parameters

• • 14.1 Introduction
• • 14.2 Related work
• • 14.3 Methodology
• • 14.3.1 Data preprocessing
• • 14.3.2 Feature engineering
• • 14.3.3 Feature selection
• • 14.3.4 Traintest split
• • 14.3.5 Machine learning models
• • 14.3.6 Performance parameters
• • 14.3.7 Results and discussion
• • 14.4 Conclusion
• • References

Chapter 15: Cancer survival prediction using artificial intelligence: current status and future prospects

• • 15.1 Introduction
• • 15.2 Literature review
• • 15.2.1 Classical machine learning techniques for cancer survival prediction
• • 15.2.2 Ensemble learning techniques for cancer survival prediction
• • 15.2.3 Deep learning techniques for cancer survival prediction
• • 15.3 Evaluation metrics for cancer survival prediction
• • 15.3.1 Classification metrics
• • 15.3.2 Discriminative metrics
• • 15.3.3 Explainability metrics
• • 15.4 Challenges and limitations of using artificial intelligence techniques
• • 15.4.1 Data availability and quality (the data dilemma)
• • 15.4.2 Interpretation and explainability (the artificial intelligence enigma)
• • 15.4.3 Ethical considerations (guardian of privacy)
• • 15.5 Conclusion and future direction
• • References

Chapter 16: Heart disease prediction in pregnant women with diabetes using machine learning

• • 16.1 Introduction
• • 16.2 Literature review
• • 16.3 Proposed research work
• • 16.3.1 Comprehensive guide to predictive modeling and machine learning
• • 16.3.2 System flow diagram for advanced heart disease prediction in diabetic pregnancy
• • 16.3.3 Potential benefits on early risk prediction during diabetic pregnancy
• • 16.3.4 Advanced machine learning approaches for early detection and risk assessment in diabetic pregnancy
• • 16.4 Results and discussion
• • 16.5 Performance metrics for machine learning models: logistic regression, random forest, and decision tree
• • 16.5.1 Novelty of proposed work
• • 16.6 Conclusion
• • 16.7 Future scope
• • AI disclosure
• • References

Chapter 17: Healthcare using image recognition technology

• • 17.1 Introduction
• • 17.1.1 What is image processing, exactly?
• • 17.1.2 What does medical image processing entail?
• • 17.1.3 What is image classification?
• • 17.1.4 How does image classification work?
• • 17.1.5 What is image processing in medicine?
• • 17.1.6 How does medical image processing work?
• • 17.2 What is machine learning and how does it work?
• • 17.2.1 Exactly what is machine learning?
• • 17.2.2 What is the process of machine learning?
• • 17.2.3 What kinds of machine learning are there?
• • 17.2.4 What is the importance of machine learning?
• • 17.2.5 Machine learning’s principal uses
• • 17.3 Master’s in healthcare
• • 17.3.1 Applications of artificial intelligence in healthcare
• • 17.4 Discussion on medical image processing
• • 17.4.1 Related resources
• • 17.5 Conclusion
• • References

Chapter 18: Integration of deep learning and blockchain technology for a smart healthcare record management system

• • 18.1 Introduction
• • 18.2 Importance of smart healthcare
• • 18.2.1 Internet of Medical Things
• • 18.2.2 Smart e-healthcare
• • 18.3 Emerging technologies in Internet of Medical Things
• • 18.3.1 Artificial intelligence in Internet of Medical Things
• • 18.3.2 Blockchain in Internet of Medical Things
• • 18.3.3 Machine learning in Internet of Medical Things
• • 18.3.4 Cloud computing in Internet of Medical Things
• • 18.4 Digital twins, telemedicine, and metaverse in Internet of Medical Things
• • 18.5 Case study: patient centric heal