ata 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.
• Shows how improving automated analytical techniques can be used to generate new information from data for healthcare applications1. Data Science in the Medical Field - p. Page i 2. Cover - p. Page i 3. Title page - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Contents - p. Page vi 7. List of contributors - p. Page vii 8. About the authors - p. Page vii 9. Preface - p. Page viii 10. List of Illustrations - p. Page x 11. List of Tables - p. Page xi 12. Chapter 1 : PPH 4.0: a privacy-preserving health 4.0 framework with machine learning and cellular automata - p. Page xi 13. Chapter 2 : An automatic detection and severity levels of COVID-19 using convolutional neural network models - p. Page 13 14. Chapter 3 : Biosensors and disease diagnostics in medical field - p. Page 23 15. Chapter 4 : Brain tumor recognition and classification techniques - p. Page 40 16. Chapter 5 : Identifying the features and attributes of various artificial intelligence-based healthcare models - p. Page 54 17. Chapter 6 : Classification algorithms and optimization techniques in healthcare systems representation of dataset in medical applications - p. Page 74 18. Chapter 7 : A knowledge discovery framework for COVID-19 disease from PubMed abstract using association rule hypergraph - p. Page 81 19. Chapter 8 : Predictive analysis in healthcare using data science: leveraging big data for improved patient care - p. Page 95 20. Chapter 9 : Data science in medical field: advantages, challenges, and opportunities - p. Page 109 21. Chapter 10 : Decentralizing healthcare through parallel blockchain architecture: transmitting internet of medical things data through smart contracts in telecare medical information systems - p. Page 121 22. Chapter 11 : Machine learning in heart disease prediction - p. Page 148 23. Chapter 12 : U-Net-based approaches for brain tumor segmentation - p. Page 162 24. Chapter 13 : Explainable image recognition models for aiding radiologists in clinical decision making - p. Page 190 25. Chapter 14 : Prediction of heart failure disease using classification algorithms along with performance parameters - p. Page 210 26. Chapter 15 : Cancer survival prediction using artificial intelligence: current status and future prospects - p. Page 226 27. Chapter 16 : Heart disease prediction in pregnant women with diabetes using machine learning - p. Page 238 28. Chapter 17 : Healthcare using image recognition technology - p. Page 258 29. Chapter 18 : Integration of deep learning and blockchain technology for a smart healthcare record management system - p. Page 273 30. Chapter 19 : Internet of things based smart health and attendance monitoring system in an institution for COVID-19 - p. Page 287 31. Chapter 20 : Medical diagnosis using image processing techniques - p. Page 300 32. Chapter 21 : Harnessing the potential of predictive analytics and machine learning in healthcare: empowering clinical research and patient care - p. Page 315 33. Chapter 22 : Predictive analysis in healthcare using data science - p. Page 327 34. Chapter 23 : Recommender systems in healthcare—an emerging technology - p. Page 343 35. Chapter 24 : Robotics: challenges and opportunities in healthcare - p. Page 358 36. Chapter 25 : A new era of the healthcare industry using Internet of Medical Things - p. Page 366 37. Chapter 26 : Single cell genomics unleashed: exploring the landscape of endometriosis with machine learning, gene expression profiling, and therapeutic target discovery - p. Page 375 38. Chapter 27 : Analyzing the success of the thriving machine prediction model for Parkinson’s disease prognosis: a comprehensive review - p. Page 388 39. Author Index - p. Page 402 40. Subject Index - p. Page 402
ata 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.
• Shows how improving automated analytical techniques can be used to generate new information from data for healthcare applications1. Data Science in the Medical Field - p. Page i 2. Cover - p. Page i 3. Title page - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Contents - p. Page vi 7. List of contributors - p. Page vii 8. About the authors - p. Page vii 9. Preface - p. Page viii 10. List of Illustrations - p. Page x 11. List of Tables - p. Page xi 12. Chapter 1 : PPH 4.0: a privacy-preserving health 4.0 framework with machine learning and cellular automata - p. Page xi 13. Chapter 2 : An automatic detection and severity levels of COVID-19 using convolutional neural network models - p. Page 13 14. Chapter 3 : Biosensors and disease diagnostics in medical field - p. Page 23 15. Chapter 4 : Brain tumor recognition and classification techniques - p. Page 40 16. Chapter 5 : Identifying the features and attributes of various artificial intelligence-based healthcare models - p. Page 54 17. Chapter 6 : Classification algorithms and optimization techniques in healthcare systems representation of dataset in medical applications - p. Page 74 18. Chapter 7 : A knowledge discovery framework for COVID-19 disease from PubMed abstract using association rule hypergraph - p. Page 81 19. Chapter 8 : Predictive analysis in healthcare using data science: leveraging big data for improved patient care - p. Page 95 20. Chapter 9 : Data science in medical field: advantages, challenges, and opportunities - p. Page 109 21. Chapter 10 : Decentralizing healthcare through parallel blockchain architecture: transmitting internet of medical things data through smart contracts in telecare medical information systems - p. Page 121 22. Chapter 11 : Machine learning in heart disease prediction - p. Page 148 23. Chapter 12 : U-Net-based approaches for brain tumor segmentation - p. Page 162 24. Chapter 13 : Explainable image recognition models for aiding radiologists in clinical decision making - p. Page 190 25. Chapter 14 : Prediction of heart failure disease using classification algorithms along with performance parameters - p. Page 210 26. Chapter 15 : Cancer survival prediction using artificial intelligence: current status and future prospects - p. Page 226 27. Chapter 16 : Heart disease prediction in pregnant women with diabetes using machine learning - p. Page 238 28. Chapter 17 : Healthcare using image recognition technology - p. Page 258 29. Chapter 18 : Integration of deep learning and blockchain technology for a smart healthcare record management system - p. Page 273 30. Chapter 19 : Internet of things based smart health and attendance monitoring system in an institution for COVID-19 - p. Page 287 31. Chapter 20 : Medical diagnosis using image processing techniques - p. Page 300 32. Chapter 21 : Harnessing the potential of predictive analytics and machine learning in healthcare: empowering clinical research and patient care - p. Page 315 33. Chapter 22 : Predictive analysis in healthcare using data science - p. Page 327 34. Chapter 23 : Recommender systems in healthcare—an emerging technology - p. Page 343 35. Chapter 24 : Robotics: challenges and opportunities in healthcare - p. Page 358 36. Chapter 25 : A new era of the healthcare industry using Internet of Medical Things - p. Page 366 37. Chapter 26 : Single cell genomics unleashed: exploring the landscape of endometriosis with machine learning, gene expression profiling, and therapeutic target discovery - p. Page 375 38. Chapter 27 : Analyzing the success of the thriving machine prediction model for Parkinson’s disease prognosis: a comprehensive review - p. Page 388 39. Author Index - p. Page 402 40. Subject Index - p. Page 402
