Data science in the medical field

Table of content:
1. PPH 4.0: a privacy-preserving health 4.0 framework with machine learning and cellular automata
Arnab Mitra and Anabik Pal

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


2. An automatic detection and severity levels of COVID-19 using convolutional neural network models
Samba Siva Krishna Assish Yellepeddi and P. Kuppusamy

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


3. Biosensors and disease diagnostics in medical field
Harpreet Kaur Channi, Ramandeep Sandhu, Deepika Ghai, Kanav Dhir, Komal Arora and Suman Lata Tripathi

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


4. Brain tumor recognition and classification techniques
Roaa Soloh, Ali Rammal and Mohamad El-Abed

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


5. Identifying the features and attributes of various artificial intelligence-based healthcare models
Nisha Soms, David Samuel Azariya S. and Abhinaya Saravanan A.

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


6. Classification algorithms and optimization techniques in healthcare systems representation of dataset in medical applications
P. Deivendran, S. Muthukaruppasamy, G. Arun Sampaul Thomas and K. Saravanan

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


7. A knowledge discovery framework for COVID-19 disease from PubMed abstract using association rule hypergraph
Pradeepa Sampath, Vimal Shanmuganathan, Janmenjoy Nayak, Subbulakshmi Pasupathi, Prasun Chakrabarti and Kaliappan Madasamy

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


8. Predictive analysis in healthcare using data science: leveraging big data for improved patient care
Hirak Mazumdar and Kamil Reza Khondakar

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


9. Data science in medical field: advantages, challenges, and opportunities
S. Geetha, J. Madhusudanan and V. Prasanna Venkatesan

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


10. Decentralizing healthcare through parallel blockchain architecture: transmitting internet of medical things data through smart contracts in telecare medical information systems
Sebastian Melbye and Sahar Yassine

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


11. Machine learning in heart disease prediction
Delshi Howsalya Devi R., R. Praveen and A. Asis Jovin

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


12. U-Net-based approaches for brain tumor segmentation
Vegard Eikenes and Seifedine Kadry

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


13. Explainable image recognition models for aiding radiologists in clinical decision making
Auxilia Michael, Abarna Vasanth, Feron Arockiam Sagayaradjy, Mohammed Feroz and Rahul Gnanapragasam

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


14. Prediction of heart failure disease using classification algorithms along with performance parameters
Karthika Natarajan and C. Rajeev

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


15. Cancer survival prediction using artificial intelligence: current status and future prospects
Hasan Shaikh and Rashid Ali

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


16. Heart disease prediction in pregnant women with diabetes using machine learning
Sujatha Rajkumar, Svetlana Stanarevic, Yogeshwar P, Karthikeyan BM and Kaviya V

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


17. Healthcare using image recognition technology
Karthika Natarajan and SivaTejaswi Jonna

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


18. Integration of deep learning and blockchain technology for a smart healthcare record management system
Sujatha Rajkumar, Vandana Mansur, Akshat, Yashraj Motwani, Vinod Salunkhe and Thomas M. Chen

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 healthcare model

18.5.1 Healthcare data analysis using deep learning-based segmentation and classification model

18.5.2 Blockchain-based electronic health record for medical record data storage

18.6 Results

18.6.1 Classification model performance of medical images

18.6.2 Security measures on Ethereum blockchain-based attacks

18.6.3 Ethereum blockchain evaluation metrics

18.7 Discussion

18.8 Conclusion
References


19. Internet of things based smart health and attendance monitoring system in an institution for COVID-19
C.M. Arun Kumar, Senthilkumar Subramaniyan and C. Kavitha

19.1 Introduction

19.2 Coronavirus

19.2.1 Smart healthcare services

19.2.2 Proposed system design

19.3 Different technologies

19.3.1 Evolution of Internet of Things

19.3.2 Block diagram

19.3.3 Mask detection unit

19.4 Result and discussion

19.4.1 Mask detection

19.4.2 Attendance report

19.5 Conclusions
References


20. Medical diagnosis using image processing techniques
Aavampreet Kour

20.1 Introduction

20.2 Image processing techniques in medical diagnosis

20.2.1 Image acquisition

20.2.2 Preprocessing

20.2.3 Feature extraction

20.2.4 Classification

20.3 Recent advancements in medical diagnosis

20.3.1 Computer-aided diagnosis systems

20.3.2 Deep learning-based approach

20.3.3 Transfer learning

20.3.4 Multimodal fusion techniques

20.3.5 Real-time diagnosis

20.4 Methodology and applications

20.4.1 Chest X-ray analysis for pneumonia detection

20.4.2 Mammography interpretation through image processing

20.5 Advancements in image processing

20.5.1 Improved accuracy

20.5.2 Faster diagnosis

20.5.3 Enhanced visualization

20.5.4 Remote diagnoses and telemedicine

20.5.5 Integration with artificial intelligence and machine learning

20.6 Challenges in medical diagnosis using image processing

20.6.1 Data quality and availability

20.6.2 Interpretability and explainability

20.6.3 Limited labeled data

20.6.4 Integration of various imaging modalities

20.6.5 Real-time processing and computational efficiency

20.7 Potential solutions and future directions

20.7.1 Data synthesis and augmentation methods

20.7.2 Explainable artificial intelligence methodologies

20.7.3 Active and semisupervised learning

20.7.4 Multimodal and hybrid approaches

20.7.5 Edge computing and hardware optimization

20.8 Evaluation metrics and performance analysis

20.8.1 Accuracy

20.8.2 Precision

20.8.3 Sensitivity and specificity

20.8.4 F1 score

20.8.5 Analysis of the receiver operating characteristic curve

20.8.6 Performance evaluation for localization and segmentation

20.9 Conclusion
References


21. Harnessing the potential of predictive analytics and machine learning in healthcare: empowering clinical research and patient care
G. Arun Sampaul Thomas, S. Muthukaruppasamy, P. Deivendran, G. Sudha and K. Saravanan

21.1 Introduction

21.1.1 Uses of predictive analytics in healthcare

21.1.2 Benefits of predictive analytics in healthcare

21.2 Healthcare predictive modeling

21.3 The use of machine learning in the medical business

21.4 Healthcare predictive analytics example

21.4.1 Examples of predictive analytics used in the healthcare industry

21.4.2 Obstacles faced by artificial intelligence and machine learning in the healthcare industry

21.4.3 Possible answers to frequent challenges in the healthcare industry

21.5 The use of predictive analytics with the use of machine learning

21.5.1 Examples of learning machines in action

21.6 Conclusion
References


22. Predictive analysis in healthcare using data science
C. Aarthy, D. Balakrishnan and Nandhagopal Subramani

22.1 Introduction

22.2 Related works

22.3 An in-depth look of data science

22.3.1 Data science on practice

22.4 Data science in the world of healthcare

22.4.1 Breast cancer

22.4.2 Selection of features

22.4.3 Initial data visualization

22.4.4 The approach of random forests to forecasting data

22.4.5 Added potential elements

22.4.6 Interpreting signals related to medicine

22.4.7 Management of patient data

22.4.8 Medical data privacy and fraud prevention

22.5 The healthcare sector

22.5.1 Expanding the analytics infrastructure

22.5.2 Using cutting-edge analytics to forecast results

22.5.3 Utilizing machine learning to analyze patient data

22.5.4 Big data management

22.5.5 Incorporating information technology into healthcare

22.6 Strategies and tools for using data science in healthcare

22.6.1 Machine learning

22.6.2 Deep learning

22.6.3 Natural language processing

22.6.4 Predictive analytics

22.6.5 Data mining

22.6.6 Big data technologies

22.6.7 Electronic health records

22.6.8 Telemedicine and remote healthcare

22.6.9 Health informatics

22.6.10 Applications for mobile Health

22.6.11 Decision support systems

22.7 A guide to data science in healthcare: applications

22.7.1 Data science for medical imaging

22.7.2 Data science for genomics

22.7.3 Data science in drug discovery

22.7.4 Predictive data analytics in healthcare

22.7.5 Monitoring patient health and data science

22.7.6 Tracking and preventing diseases with data science

22.7.7 Virtual assistance with data science

22.8 Data science’s effects on healthcare

22.8.1 Patent foramen ovale

22.8.2 Diagnostic advancements

22.8.3 Improved long-term results

22.8.4 Patient risk stratification

22.9 Healthcare benefits of data science

22.10 Challenges

22.11 Healthcare data science future

22.12 Conclusion
References


23. Recommender systems in healthcare—an emerging technology
Kusumalatha Karre and Ramadevi Y.

23.1 Introduction

23.1.1 Recommender system with various filtering techniques

23.2 Recommender Systems in Hhealthcare

23.2.1 Examples of Recommender systems in healthcare industry

23.3 Major challenges in Rrecommender Ssystems

23.3.1 Data collection

23.3.2 Evaluation metrics

23.3.3 Privacy

23.4 Conclusion
References


24. Robotics: challenges and opportunities in healthcare
Ruby Pant, Kapil Joshi and Shubham Mahajan

24.1 Introduction

24.2 Research method

24.2.1 History and overview of robot

24.3 Literature survey

24.4 Advantages of robot in healthcare sectors

24.5 Applications of robotics in healthcare

24.6 Challenges in implementing robotics in healthcare

24.7 Conclusion
References


25. A new era of the healthcare industry using Internet of Medical Things
Hamnah Rao, Parul Agarwal, Saima Naaz, Sapna Jain and Ahmed Obaid

25.1 Introduction

25.2 Structure of Internet of Things-based healthcare system

25.3 Literature review

25.4 Types of Internet of Medical Things devices

25.5 Components of Internet of Medical Things

25.6 Benefits of Internet of Medical Things

25.7 Challenges of Internet of Medical Things

25.7.1 Technical challenges

25.7.2 Financial challenges

25.7.3 Ethical challenges

25.8 Conclusion and future work
References


26. Single cell genomics unleashed: exploring the landscape of endometriosis with machine learning, gene expression profiling, and therapeutic target discovery
Sudip Mondal

26.1 Introduction

26.2 Advancement of machine learning classifiers for the study of endometriosis

26.3 Single-cell analysis of endometriosis

26.4 Gene expression analysis of endometrium

26.5 Identification of novel drug targets for endometriosis

26.6 Discussion
Acknowledgments
References


27. Analyzing the success of the thriving machine prediction model for Parkinson’s disease prognosis: a comprehensive review
Marion O. Adebiyi, Prisca O. Olawoye and Moses Abiodun

27.1 Introduction

27.2 Related works

27.3 Methods

27.3.1 Principal component analysis

27.3.2 Independent component analysis

27.3.3 Linear discriminant analysis

27.3.4 t-Distributed stochastic neighbor embedding

27.3.5 Nonnegative matrix factorization

27.3.6 Recursive feature elimination

27.3.7 SelectKBest

27.3.8 Minimum redundancy maximum relevance

27.3.9 Least absolute shrinkage and selection operator

27.3.10 Support vector machines

27.3.11 Random forest

27.3.12 K-nearest neighbor

27.3.13 Decision tree

27.3.14 Artificial neural network

27.3.15 Convolutional neural networks

27.4 Discussions

27.5 Conclusion

[https://shop.elsevier.com/books/data-science-in-the-medical-field/kadry/978-0-443-24028-7]

Data science has the potential to influence and improve fundamental services such as the healthcare sector. This book recognizes this fact by analyzing the potential uses of data science in healthcare. Every human body produces 2 TB of data each day. This information covers brain activity, stress level, heart rate, blood sugar level, and many other things. More sophisticated technology, such as data science, allows clinicians and researchers to handle such a massive volume of data to track the health of patients. The book focuses on the potential and the tools of data science to identify the signs of illness at an extremely early stage.

(https://shop.elsevier.com/books/data-science-in-the-medical-field/kadry/978-0-443-24028-7)