Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease

Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease

Author: Roy, Manikant

Publisher: IGI Global

Published: 2021-06-25

Total Pages: 241

ISBN-13: 1799871908

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Data analytics is proving to be an ally for epidemiologists as they join forces with data scientists to address the scale of crises. Analytics examined from many sources can derive insights and be used to study and fight global outbreaks. Pandemic analytics is a modern way to combat a problem as old as humanity itself: the proliferation of disease. Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease explores different types of data and discusses how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more by applying cutting edge technology such as machine learning and data analytics in the wake of the COVID-19 pandemic. Covering a range of topics such as mental health analytics during COVID-19, data analysis and machine learning using Python, and statistical model development and deployment, it is ideal for researchers, academicians, data scientists, technologists, data analysts, diagnosticians, healthcare professionals, computer scientists, and students.


Book Synopsis Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease by : Roy, Manikant

Download or read book Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease written by Roy, Manikant and published by IGI Global. This book was released on 2021-06-25 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data analytics is proving to be an ally for epidemiologists as they join forces with data scientists to address the scale of crises. Analytics examined from many sources can derive insights and be used to study and fight global outbreaks. Pandemic analytics is a modern way to combat a problem as old as humanity itself: the proliferation of disease. Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease explores different types of data and discusses how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more by applying cutting edge technology such as machine learning and data analytics in the wake of the COVID-19 pandemic. Covering a range of topics such as mental health analytics during COVID-19, data analysis and machine learning using Python, and statistical model development and deployment, it is ideal for researchers, academicians, data scientists, technologists, data analysts, diagnosticians, healthcare professionals, computer scientists, and students.

Intelligent Techniques for Predictive Data Analytics

Intelligent Techniques for Predictive Data Analytics

Author: Neha Singh

Publisher: John Wiley & Sons

Published: 2024-06-21

Total Pages: 276

ISBN-13: 1394227973

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Comprehensive resource covering tools and techniques used for predictive analytics with practical applications across various industries Intelligent Techniques for Predictive Data Analytics provides an in-depth introduction of the tools and techniques used for predictive analytics, covering applications in cyber security, network security, data mining, and machine learning across various industries. Each chapter offers a brief introduction on the subject to make the text accessible regardless of background knowledge. Readers will gain a clear understanding of how to use data processing, classification, and analysis to support strategic decisions, such as optimizing marketing strategies and customer relationship management and recommendation systems, improving general business operations, and predicting occurrence of chronic diseases for better patient management. Traditional data analytics uses dashboards to illustrate trends and outliers, but with large data sets, this process is labor-intensive and time-consuming. This book provides everything readers need to save time by performing deep, efficient analysis without human bias and time constraints. A section on current challenges in the field is also included. Intelligent Techniques for Predictive Data Analytics covers sample topics such as: Models to choose from in predictive modeling, including classification, clustering, forecast, outlier, and time series models Price forecasting, quality optimization, and insect and disease plant and monitoring in agriculture Fraud detection and prevention, credit scoring, financial planning, and customer analytics Big data in smart grids, smart grid analytics, and predictive smart grid quality monitoring, maintenance, and load forecasting Management of uncertainty in predictive data analytics and probable future developments in the field Intelligent Techniques for Predictive Data Analytics is an essential resource on the subject for professionals and researchers working in data science or data management seeking to understand the different models of predictive analytics, along with graduate students studying data science courses and professionals and academics new to the field.


Book Synopsis Intelligent Techniques for Predictive Data Analytics by : Neha Singh

Download or read book Intelligent Techniques for Predictive Data Analytics written by Neha Singh and published by John Wiley & Sons. This book was released on 2024-06-21 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive resource covering tools and techniques used for predictive analytics with practical applications across various industries Intelligent Techniques for Predictive Data Analytics provides an in-depth introduction of the tools and techniques used for predictive analytics, covering applications in cyber security, network security, data mining, and machine learning across various industries. Each chapter offers a brief introduction on the subject to make the text accessible regardless of background knowledge. Readers will gain a clear understanding of how to use data processing, classification, and analysis to support strategic decisions, such as optimizing marketing strategies and customer relationship management and recommendation systems, improving general business operations, and predicting occurrence of chronic diseases for better patient management. Traditional data analytics uses dashboards to illustrate trends and outliers, but with large data sets, this process is labor-intensive and time-consuming. This book provides everything readers need to save time by performing deep, efficient analysis without human bias and time constraints. A section on current challenges in the field is also included. Intelligent Techniques for Predictive Data Analytics covers sample topics such as: Models to choose from in predictive modeling, including classification, clustering, forecast, outlier, and time series models Price forecasting, quality optimization, and insect and disease plant and monitoring in agriculture Fraud detection and prevention, credit scoring, financial planning, and customer analytics Big data in smart grids, smart grid analytics, and predictive smart grid quality monitoring, maintenance, and load forecasting Management of uncertainty in predictive data analytics and probable future developments in the field Intelligent Techniques for Predictive Data Analytics is an essential resource on the subject for professionals and researchers working in data science or data management seeking to understand the different models of predictive analytics, along with graduate students studying data science courses and professionals and academics new to the field.

AI and Machine Learning Paradigms for Health Monitoring System

AI and Machine Learning Paradigms for Health Monitoring System

Author: Hasmat Malik

Publisher: Springer Nature

Published: 2021-02-14

Total Pages: 513

ISBN-13: 9813344121

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This book embodies principles and applications of advanced soft computing approaches in engineering, healthcare and allied domains directed toward the researchers aspiring to learn and apply intelligent data analytics techniques. The first part covers AI, machine learning and data analytics tools and techniques and their applications to the class of several hospital and health real-life problems. In the later part, the applications of AI, ML and data analytics shall be covered over the wide variety of applications in hospital, health, engineering and/or applied sciences such as the clinical services, medical image analysis, management support, quality analysis, bioinformatics, device analysis and operations. The book presents knowledge of experts in the form of chapters with the objective to introduce the theme of intelligent data analytics and discusses associated theoretical applications. At last, it presents simulation codes for the problems included in the book for better understanding for beginners.


Book Synopsis AI and Machine Learning Paradigms for Health Monitoring System by : Hasmat Malik

Download or read book AI and Machine Learning Paradigms for Health Monitoring System written by Hasmat Malik and published by Springer Nature. This book was released on 2021-02-14 with total page 513 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book embodies principles and applications of advanced soft computing approaches in engineering, healthcare and allied domains directed toward the researchers aspiring to learn and apply intelligent data analytics techniques. The first part covers AI, machine learning and data analytics tools and techniques and their applications to the class of several hospital and health real-life problems. In the later part, the applications of AI, ML and data analytics shall be covered over the wide variety of applications in hospital, health, engineering and/or applied sciences such as the clinical services, medical image analysis, management support, quality analysis, bioinformatics, device analysis and operations. The book presents knowledge of experts in the form of chapters with the objective to introduce the theme of intelligent data analytics and discusses associated theoretical applications. At last, it presents simulation codes for the problems included in the book for better understanding for beginners.

Big Data Analytics and Intelligence

Big Data Analytics and Intelligence

Author: Poonam Tanwar

Publisher: Emerald Group Publishing

Published: 2020-09-30

Total Pages: 252

ISBN-13: 1839091010

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Big Data Analytics and Intelligence is essential reading for researchers and experts working in the fields of health care, data science, analytics, the internet of things, and information retrieval.


Book Synopsis Big Data Analytics and Intelligence by : Poonam Tanwar

Download or read book Big Data Analytics and Intelligence written by Poonam Tanwar and published by Emerald Group Publishing. This book was released on 2020-09-30 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Analytics and Intelligence is essential reading for researchers and experts working in the fields of health care, data science, analytics, the internet of things, and information retrieval.

Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms

Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms

Author: Milutinovi?, Veljko

Publisher: IGI Global

Published: 2022-03-11

Total Pages: 296

ISBN-13: 1799883523

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Based on current literature and cutting-edge advances in the machine learning field, there are four algorithms whose usage in new application domains must be explored: neural networks, rule induction algorithms, tree-based algorithms, and density-based algorithms. A number of machine learning related algorithms have been derived from these four algorithms. Consequently, they represent excellent underlying methods for extracting hidden knowledge from unstructured data, as essential data mining tasks. Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms presents widely used data-mining algorithms and explains their advantages and disadvantages, their mathematical treatment, applications, energy efficient implementations, and more. It presents research of energy efficient accelerators for machine learning algorithms. Covering topics such as control-flow implementation, approximate computing, and decision tree algorithms, this book is an essential resource for computer scientists, engineers, students and educators of higher education, researchers, and academicians.


Book Synopsis Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms by : Milutinovi?, Veljko

Download or read book Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms written by Milutinovi?, Veljko and published by IGI Global. This book was released on 2022-03-11 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Based on current literature and cutting-edge advances in the machine learning field, there are four algorithms whose usage in new application domains must be explored: neural networks, rule induction algorithms, tree-based algorithms, and density-based algorithms. A number of machine learning related algorithms have been derived from these four algorithms. Consequently, they represent excellent underlying methods for extracting hidden knowledge from unstructured data, as essential data mining tasks. Implementation of Machine Learning Algorithms Using Control-Flow and Dataflow Paradigms presents widely used data-mining algorithms and explains their advantages and disadvantages, their mathematical treatment, applications, energy efficient implementations, and more. It presents research of energy efficient accelerators for machine learning algorithms. Covering topics such as control-flow implementation, approximate computing, and decision tree algorithms, this book is an essential resource for computer scientists, engineers, students and educators of higher education, researchers, and academicians.

Disease Prediction using Machine Learning, Deep Learning and Data Analytics

Disease Prediction using Machine Learning, Deep Learning and Data Analytics

Author: Geeta Rani, Vijaypal Singh Dhaka, Pradeep Kumar Tiwari

Publisher: Bentham Science Publishers

Published: 2024-03-07

Total Pages: 196

ISBN-13: 9815179136

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This book is a comprehensive review of technologies and data in healthcare services. It features a compilation of 10 chapters that inform readers about the recent research and developments in this field. Each chapter focuses on a specific aspect of healthcare services, highlighting the potential impact of technology on enhancing practices and outcomes. The main features of the book include 1) referenced contributions from healthcare and data analytics experts, 2) a broad range of topics that cover healthcare services, and 3) demonstration of deep learning techniques for specific diseases. Key topics: - Federated learning in analysis of sensitive healthcare data while preserving privacy and security. - Artificial intelligence for 3-D bone image reconstruction. - Detection of disease severity and creating personalized treatment plans using machine learning and software tools - Case studies for disease detection methods for different disease and conditions, including dementia, asthma, eye diseases - Brain-computer interfaces - Data mining for standardized electronic health records - Data collection, management, and analysis in epidemiological research The book is a resource for learners and professionals in healthcare service training programs and health administration departments. Readership Learners and professionals in healthcare service training programs and health administration departments.


Book Synopsis Disease Prediction using Machine Learning, Deep Learning and Data Analytics by : Geeta Rani, Vijaypal Singh Dhaka, Pradeep Kumar Tiwari

Download or read book Disease Prediction using Machine Learning, Deep Learning and Data Analytics written by Geeta Rani, Vijaypal Singh Dhaka, Pradeep Kumar Tiwari and published by Bentham Science Publishers. This book was released on 2024-03-07 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a comprehensive review of technologies and data in healthcare services. It features a compilation of 10 chapters that inform readers about the recent research and developments in this field. Each chapter focuses on a specific aspect of healthcare services, highlighting the potential impact of technology on enhancing practices and outcomes. The main features of the book include 1) referenced contributions from healthcare and data analytics experts, 2) a broad range of topics that cover healthcare services, and 3) demonstration of deep learning techniques for specific diseases. Key topics: - Federated learning in analysis of sensitive healthcare data while preserving privacy and security. - Artificial intelligence for 3-D bone image reconstruction. - Detection of disease severity and creating personalized treatment plans using machine learning and software tools - Case studies for disease detection methods for different disease and conditions, including dementia, asthma, eye diseases - Brain-computer interfaces - Data mining for standardized electronic health records - Data collection, management, and analysis in epidemiological research The book is a resource for learners and professionals in healthcare service training programs and health administration departments. Readership Learners and professionals in healthcare service training programs and health administration departments.

Big Data Analytics for Healthcare

Big Data Analytics for Healthcare

Author: Pantea Keikhosrokiani

Publisher: Academic Press

Published: 2022-05-19

Total Pages: 356

ISBN-13: 0323985165

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Big Data Analytics and Medical Information Systems presents the valuable use of artificial intelligence and big data analytics in healthcare and medical sciences. It focuses on theories, methods and approaches in which data analytic techniques can be used to examine medical data to provide a meaningful pattern for classification, diagnosis, treatment, and prediction of diseases. The book discusses topics such as theories and concepts of the field, and how big medical data mining techniques and applications can be applied to classification, diagnosis, treatment, and prediction of diseases. In addition, it covers social, behavioral, and medical fake news analytics to prevent medical misinformation and myths. It is a valuable resource for graduate students, researchers and members of biomedical field who are interested in learning more about analytic tools to support their work. Presents theories, methods and approaches in which data analytic techniques are used for medical data Brings practical information on how to use big data for classification, diagnosis, treatment, and prediction of diseases Discusses social, behavioral, and medical fake news analytics for medical information systems


Book Synopsis Big Data Analytics for Healthcare by : Pantea Keikhosrokiani

Download or read book Big Data Analytics for Healthcare written by Pantea Keikhosrokiani and published by Academic Press. This book was released on 2022-05-19 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Analytics and Medical Information Systems presents the valuable use of artificial intelligence and big data analytics in healthcare and medical sciences. It focuses on theories, methods and approaches in which data analytic techniques can be used to examine medical data to provide a meaningful pattern for classification, diagnosis, treatment, and prediction of diseases. The book discusses topics such as theories and concepts of the field, and how big medical data mining techniques and applications can be applied to classification, diagnosis, treatment, and prediction of diseases. In addition, it covers social, behavioral, and medical fake news analytics to prevent medical misinformation and myths. It is a valuable resource for graduate students, researchers and members of biomedical field who are interested in learning more about analytic tools to support their work. Presents theories, methods and approaches in which data analytic techniques are used for medical data Brings practical information on how to use big data for classification, diagnosis, treatment, and prediction of diseases Discusses social, behavioral, and medical fake news analytics for medical information systems

New Approaches to Data Analytics and Internet of Things Through Digital Twin

New Approaches to Data Analytics and Internet of Things Through Digital Twin

Author: Karthikeyan, P.

Publisher: IGI Global

Published: 2022-09-30

Total Pages: 326

ISBN-13: 1668457245

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Even though many data analytics tools have been developed in the past years, their usage in the field of cyber twin warrants new approaches that consider various aspects including unified data representation, zero-day attack detection, data sharing across threat detection systems, real-time analysis, sampling, dimensionality reduction, resource-constrained data processing, and time series analysis for anomaly detection. Further study is required to fully understand the opportunities, benefits, and difficulties of data analytics and the internet of things in today’s modern world. New Approaches to Data Analytics and Internet of Things Through Digital Twin considers how data analytics and the internet of things can be used successfully within the field of digital twin as well as the potential future directions of these technologies. Covering key topics such as edge networks, deep learning, intelligent data analytics, and knowledge discovery, this reference work is ideal for computer scientists, industry professionals, researchers, scholars, practitioners, academicians, instructors, and students.


Book Synopsis New Approaches to Data Analytics and Internet of Things Through Digital Twin by : Karthikeyan, P.

Download or read book New Approaches to Data Analytics and Internet of Things Through Digital Twin written by Karthikeyan, P. and published by IGI Global. This book was released on 2022-09-30 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: Even though many data analytics tools have been developed in the past years, their usage in the field of cyber twin warrants new approaches that consider various aspects including unified data representation, zero-day attack detection, data sharing across threat detection systems, real-time analysis, sampling, dimensionality reduction, resource-constrained data processing, and time series analysis for anomaly detection. Further study is required to fully understand the opportunities, benefits, and difficulties of data analytics and the internet of things in today’s modern world. New Approaches to Data Analytics and Internet of Things Through Digital Twin considers how data analytics and the internet of things can be used successfully within the field of digital twin as well as the potential future directions of these technologies. Covering key topics such as edge networks, deep learning, intelligent data analytics, and knowledge discovery, this reference work is ideal for computer scientists, industry professionals, researchers, scholars, practitioners, academicians, instructors, and students.

Reinventing Clinical Decision Support

Reinventing Clinical Decision Support

Author: Paul Cerrato

Publisher: Taylor & Francis

Published: 2020-01-06

Total Pages: 164

ISBN-13: 1000055558

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This book takes an in-depth look at the emerging technologies that are transforming the way clinicians manage patients, while at the same time emphasizing that the best practitioners use both artificial and human intelligence to make decisions. AI and machine learning are explored at length, with plain clinical English explanations of convolutional neural networks, back propagation, and digital image analysis. Real-world examples of how these tools are being employed are also discussed, including their value in diagnosing diabetic retinopathy, melanoma, breast cancer, cancer metastasis, and colorectal cancer, as well as in managing severe sepsis. With all the enthusiasm about AI and machine learning, it was also necessary to outline some of criticisms, obstacles, and limitations of these new tools. Among the criticisms discussed: the relative lack of hard scientific evidence supporting some of the latest algorithms and the so-called black box problem. A chapter on data analytics takes a deep dive into new ways to conduct subgroup analysis and how it’s forcing healthcare executives to rethink the way they apply the results of large clinical trials to everyday medical practice. This re-evaluation is slowly affecting the way diabetes, heart disease, hypertension, and cancer are treated. The research discussed also suggests that data analytics will impact emergency medicine, medication management, and healthcare costs. An examination of the diagnostic reasoning process itself looks at how diagnostic errors are measured, what technological and cognitive errors are to blame, and what solutions are most likely to improve the process. It explores Type 1 and Type 2 reasoning methods; cognitive mistakes like availability bias, affective bias, and anchoring; and potential solutions such as the Human Diagnosis Project. Finally, the book explores the role of systems biology and precision medicine in clinical decision support and provides several case studies of how next generation AI is transforming patient care.


Book Synopsis Reinventing Clinical Decision Support by : Paul Cerrato

Download or read book Reinventing Clinical Decision Support written by Paul Cerrato and published by Taylor & Francis. This book was released on 2020-01-06 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book takes an in-depth look at the emerging technologies that are transforming the way clinicians manage patients, while at the same time emphasizing that the best practitioners use both artificial and human intelligence to make decisions. AI and machine learning are explored at length, with plain clinical English explanations of convolutional neural networks, back propagation, and digital image analysis. Real-world examples of how these tools are being employed are also discussed, including their value in diagnosing diabetic retinopathy, melanoma, breast cancer, cancer metastasis, and colorectal cancer, as well as in managing severe sepsis. With all the enthusiasm about AI and machine learning, it was also necessary to outline some of criticisms, obstacles, and limitations of these new tools. Among the criticisms discussed: the relative lack of hard scientific evidence supporting some of the latest algorithms and the so-called black box problem. A chapter on data analytics takes a deep dive into new ways to conduct subgroup analysis and how it’s forcing healthcare executives to rethink the way they apply the results of large clinical trials to everyday medical practice. This re-evaluation is slowly affecting the way diabetes, heart disease, hypertension, and cancer are treated. The research discussed also suggests that data analytics will impact emergency medicine, medication management, and healthcare costs. An examination of the diagnostic reasoning process itself looks at how diagnostic errors are measured, what technological and cognitive errors are to blame, and what solutions are most likely to improve the process. It explores Type 1 and Type 2 reasoning methods; cognitive mistakes like availability bias, affective bias, and anchoring; and potential solutions such as the Human Diagnosis Project. Finally, the book explores the role of systems biology and precision medicine in clinical decision support and provides several case studies of how next generation AI is transforming patient care.

Management for Digital Transformation

Management for Digital Transformation

Author: Carolina Machado

Publisher: Springer Nature

Published: 2023-10-25

Total Pages: 226

ISBN-13: 3031420608

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This book is a comprehensive resource for managers, engineers, researchers, academics, and professionals from various fields seeking to grasp the complexities and opportunities presented by digital transformation. It goes beyond the superficial understanding of digitalization, delving into the intricacies of this transformative process and its profound impact on organizations. By exploring the latest developments and insights from around the world, readers will gain a deep understanding of how digital transformation influences not only technological aspects but also human resources, processes, relationships, and information management. With a critical lens, this book addresses the challenges and changes that arise in the context of digital transformation, empowering readers to effectively lead and manage these processes. From examining the role of technology transfer to discussing talent management, consumer vulnerabilities, generative AIs, and the evolving landscape of e-commerce and internet use, this book provides a rich tapestry of knowledge and practical recommendations. It also highlights the significance of collaboration, virtual teams, and intelligent tools in driving digitalization. Moreover, it explores innovative management practices and techniques for addressing mobile phone waste, utilizing scientometric, bibliometric, and visual analytic tools. Ultimately, this book equips readers with the necessary insights and strategies to navigate the digital transformation successfully and harness its potential to achieve organizational excellence in an increasingly dynamic world.


Book Synopsis Management for Digital Transformation by : Carolina Machado

Download or read book Management for Digital Transformation written by Carolina Machado and published by Springer Nature. This book was released on 2023-10-25 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a comprehensive resource for managers, engineers, researchers, academics, and professionals from various fields seeking to grasp the complexities and opportunities presented by digital transformation. It goes beyond the superficial understanding of digitalization, delving into the intricacies of this transformative process and its profound impact on organizations. By exploring the latest developments and insights from around the world, readers will gain a deep understanding of how digital transformation influences not only technological aspects but also human resources, processes, relationships, and information management. With a critical lens, this book addresses the challenges and changes that arise in the context of digital transformation, empowering readers to effectively lead and manage these processes. From examining the role of technology transfer to discussing talent management, consumer vulnerabilities, generative AIs, and the evolving landscape of e-commerce and internet use, this book provides a rich tapestry of knowledge and practical recommendations. It also highlights the significance of collaboration, virtual teams, and intelligent tools in driving digitalization. Moreover, it explores innovative management practices and techniques for addressing mobile phone waste, utilizing scientometric, bibliometric, and visual analytic tools. Ultimately, this book equips readers with the necessary insights and strategies to navigate the digital transformation successfully and harness its potential to achieve organizational excellence in an increasingly dynamic world.