TY - BOOK AU - Kadry, Seifedine AU - Mahajan, Shubham TI - Data science in the medical field SN - 978043240287 U1 - 610.28 PY - 2025/// CY - Cambridge PB - Academic Press KW - Medical statistics KW - Medical informatics N1 - 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] N2 - 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) ER -