Advanced Classification Techniques for Healthcare Analysis
Advanced Classification Techniques for Healthcare Analysis

Author: Chakraborty, Chinmay

Publisher: IGI Global

Published: 2019-02-22

Total Pages: 424

ISBN-13: 1522577971

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Medical and information communication technology professionals are working to develop robust classification techniques, especially in healthcare data/image analysis, to ensure quick diagnoses and treatments to patients. Without fast and immediate access to healthcare databases and information, medical professionals’ success rates and treatment options become limited and fall to disastrous levels. Advanced Classification Techniques for Healthcare Analysis provides emerging insight into classification techniques in delivering quality, accurate, and affordable healthcare, while also discussing the impact health data has on medical treatments. Featuring coverage on a broad range of topics such as early diagnosis, brain-computer interface, metaheuristic algorithms, clustering techniques, learning schemes, and mobile telemedicine, this book is ideal for medical professionals, healthcare administrators, engineers, researchers, academicians, and technology developers seeking current research on furthering information and communication technology that improves patient care.

Handbook on Intelligent Healthcare Analytics
Handbook on Intelligent Healthcare Analytics

Author: A. Jaya

Publisher: John Wiley & Sons

Published: 2022-05-09

Total Pages: 448

ISBN-13: 1119792533

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HANDBOOK OF INTELLIGENT HEALTHCARE ANALYTICS The book explores the various recent tools and techniques used for deriving knowledge from healthcare data analytics for researchers and practitioners. The power of healthcare data analytics is being increasingly used in the industry. Advanced analytics techniques are used against large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information. A Handbook on Intelligent Healthcare Analytics covers both the theory and application of the tools, techniques, and algorithms for use in big data in healthcare and clinical research. It provides the most recent research findings to derive knowledge using big data analytics, which helps to analyze huge amounts of real-time healthcare data, the analysis of which can provide further insights in terms of procedural, technical, medical, and other types of improvements in healthcare. In addition, the reader will find in this Handbook: Innovative hybrid machine learning and deep learning techniques applied in various healthcare data sets, as well as various kinds of machine learning algorithms existing such as supervised, unsupervised, semi-supervised, reinforcement learning, and guides how readers can implement the Python environment for machine learning; An exploration of predictive analytics in healthcare; The various challenges for smart healthcare, including privacy, confidentiality, authenticity, loss of information, attacks, etc., that create a new burden for providers to maintain compliance with healthcare data security. In addition, this book also explores various sources of personalized healthcare data and the commercial platforms for healthcare data analytics. Audience Healthcare professionals, researchers, and practitioners who wish to figure out the core concepts of smart healthcare applications and the innovative methods and technologies used in healthcare will all benefit from this book.

Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis
Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis

Author: Nilanjan Dey

Publisher: Academic Press

Published: 2019-07-31

Total Pages: 218

ISBN-13: 0128180056

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Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis covers the most current advances on how to apply classification techniques to a wide variety of clinical applications that are appropriate for researchers and biomedical engineers in the areas of machine learning, deep learning, data analysis, data management and computer-aided diagnosis (CAD) systems design. The book covers several complex image classification problems using pattern recognition methods, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Bayesian Networks (BN) and deep learning. Further, numerous data mining techniques are discussed, as they have proven to be good classifiers for medical images. Examines the methodology of classification of medical images that covers the taxonomy of both supervised and unsupervised models, algorithms, applications and challenges Discusses recent advances in Artificial Neural Networks, machine learning, and deep learning in clinical applications Introduces several techniques for medical image processing and analysis for CAD systems design

Improving Medical Data Classification with Learning Algorithms
Improving Medical Data Classification with Learning Algorithms

Author: Tarle Sukadeo

Publisher: Meem Publishers

Published: 2023-07-10

Total Pages: 0

ISBN-13: 9784473160355

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This focuses on enhancing the classification of medical data using learning algorithms. With the increasing availability and complexity of medical data, accurate and efficient classification techniques are crucial for effective healthcare decision-making. The research aims to explore various learning algorithms and their potential to improve the classification accuracy of medical data. By leveraging machine learning algorithms, this research seeks to optimize the process of categorizing medical data into specific classes or categories. The study will investigate the performance and effectiveness of different algorithms, such as decision trees, support vector machines, neural networks, and ensemble methods. These algorithms will be evaluated based on their ability to handle diverse medical data types, including patient records, diagnostic reports, medical images, and laboratory results. The outcomes of this research have the potential to contribute significantly to the field of medical data analysis. The enhanced classification techniques can help healthcare professionals accurately interpret and utilize medical data, leading to improved diagnoses, treatment planning, and patient care. Additionally, the findings may pave the way for developing automated systems that can assist medical professionals in data-driven decision-making, reducing human errors and enhancing overall healthcare efficiency. Overall, its aims to advance the field of medical data classification by leveraging learning algorithms to achieve more accurate and reliable results. The research findings have the potential to positively impact healthcare practices, facilitating better healthcare outcomes and improving patient well-being.

Advanced Prognostic Predictive Modelling in Healthcare Data Analytics
Advanced Prognostic Predictive Modelling in Healthcare Data Analytics

Author: Sudipta Roy

Publisher: Springer Nature

Published: 2021-04-22

Total Pages: 317

ISBN-13: 9811605386

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This book discusses major technical advancements and research findings in the field of prognostic modelling in healthcare image and data analysis. The use of prognostic modelling as predictive models to solve complex problems of data mining and analysis in health care is the feature of this book. The book examines the recent technologies and studies that reached the practical level and becoming available in preclinical and clinical practices in computational intelligence. The main areas of interest covered in this book are highest quality, original work that contributes to the basic science of processing, analysing and utilizing all aspects of advanced computational prognostic modelling in healthcare image and data analysis.

Performance Analysis of Data Mining Classification Techniques
Performance Analysis of Data Mining Classification Techniques

Author: Shelly Gupta

Publisher: LAP Lambert Academic Publishing

Published: 2012

Total Pages: 76

ISBN-13: 9783848438754

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The present study aimed to do the performance analysis of several data mining classification techniques using three different machine learning tools over the healthcare datasets. In this study, different data mining classification techniques have been tested on four different healthcare datasets. The standards used are percentage of accuracy and error rate of every applied classification technique. The experiments are done using the 10 fold cross validation method. A suitable technique for a particular dataset is chosen based on highest classification accuracy and least error rate.

Concepts of Artificial Intelligence and its Application in Modern Healthcare Systems
Concepts of Artificial Intelligence and its Application in Modern Healthcare Systems

Author: Deepshikha Agarwal

Publisher: CRC Press

Published: 2023-07-31

Total Pages: 362

ISBN-13: 1000906000

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This reference text presents the usage of artificial intelligence in healthcare and discusses the challenges and solutions of using advanced techniques like wearable technologies and image processing in the sector. Features: Focuses on the use of artificial intelligence (AI) in healthcare with issues, applications, and prospects Presents the application of artificial intelligence in medical imaging, fractionalization of early lung tumour detection using a low intricacy approach, etc Discusses an artificial intelligence perspective on wearable technology Analyses cardiac dynamics and assessment of arrhythmia by classifying heartbeat using electrocardiogram (ECG) Elaborates machine learning models for early diagnosis of depressive mental affliction This book serves as a reference for students and researchers analyzing healthcare data. It can also be used by graduate and post graduate students as an elective course.

Smart Medical Data Sensing and IoT Systems Design in Healthcare
Smart Medical Data Sensing and IoT Systems Design in Healthcare

Author: Chakraborty, Chinmay

Publisher: IGI Global

Published: 2019-09-20

Total Pages: 288

ISBN-13: 1799802620

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Smart healthcare technology improves the diagnosis and treatment of patients, provides easy access to medical facilities and emergency care services, and minimizes the gaps between patients and healthcare providers. While clinical data protection remains a major challenge, innovations such as the internet of medical things and smart healthcare systems increase the efficiency and quality of patient care. Healthcare technology can only become faster, more profitable, and more flexible as additional research on its advancements is conducted and collected. Smart Medical Data Sensing and IoT Systems Design in Healthcare is an essential reference source that focuses on robust and easy solutions for the delivery of medical information from patients to doctors and explores low-cost, high-performance, highly efficient, deployable IoT system options in healthcare systems. Featuring research on topics such as hospital management systems, electronic health records, and bio-signals, this book is ideally designed for technologists, engineers, scientists, clinicians, biomedical engineers, hospital directors, doctors, nurses, healthcare practitioners, telemedical agents, students, and academicians seeking coverage on the latest technological developments in medical data analysis and connectivity.

Applied Machine Learning and Multi-Criteria Decision-Making in Healthcare
Applied Machine Learning and Multi-Criteria Decision-Making in Healthcare

Author: Ilker Ozsahin

Publisher: Bentham Science Publishers

Published: 2021-11-18

Total Pages: 316

ISBN-13: 168108872X

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This book provides an ideal foundation for readers to understand the application of artificial intelligence (AI) and machine learning (ML) techniques to expert systems in the healthcare sector. It starts with an introduction to the topic and presents chapters which progressively explain decision-making theory that helps solve problems which have multiple criteria that can affect the outcome of a decision. Key aspects of the subject such as machine learning in healthcare, prediction techniques, mathematical models and classification of healthcare problems are included along with chapters which delve in to advanced topics on data science (deep-learning, artificial neural networks, etc.) and practical examples (influenza epidemiology and retinoblastoma treatment analysis). Key Features: - Introduces readers to the basics of AI and ML in expert systems for healthcare - Focuses on a problem solving approach to the topic - Provides information on relevant decision-making theory and data science used in the healthcare industry - Includes practical applications of AI and ML for advanced readers - Includes bibliographic references for further reading The reference is an accessible source of knowledge on multi-criteria decision-support systems in healthcare for medical consultants, healthcare policy makers, researchers in the field of medical biotechnology, oncology and pharmaceutical research and development.

Machine Learning and Deep Learning in Efficacy Improvement of Healthcare Systems
Machine Learning and Deep Learning in Efficacy Improvement of Healthcare Systems

Author: Om Prakash Jena

Publisher: CRC Press

Published: 2022-05-18

Total Pages: 321

ISBN-13: 1000486826

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The goal of medical informatics is to improve life expectancy, disease diagnosis and quality of life. Medical devices have revolutionized healthcare and have led to the modern age of machine learning, deep learning and Internet of Medical Things (IoMT) with their proliferation, mobility and agility. This book exposes different dimensions of applications for computational intelligence and explains its use in solving various biomedical and healthcare problems in the real world. This book describes the fundamental concepts of machine learning and deep learning techniques in a healthcare system. The aim of this book is to describe how deep learning methods are used to ensure high-quality data processing, medical image and signal analysis and improved healthcare applications. This book also explores different dimensions of computational intelligence applications and illustrates its use in the solution of assorted real-world biomedical and healthcare problems. Furthermore, it provides the healthcare sector with innovative advances in theory, analytical approaches, numerical simulation, statistical analysis, modelling, advanced deployment, case studies, analytical results, computational structuring and significant progress in the field of machine learning and deep learning in healthcare applications. FEATURES Explores different dimensions of computational intelligence applications and illustrates its use in the solution of assorted real-world biomedical and healthcare problems Provides guidance in developing intelligence-based diagnostic systems, efficient models and cost-effective machines Provides the latest research findings, solutions to the concerning issues and relevant theoretical frameworks in the area of machine learning and deep learning for healthcare systems Describes experiences and findings relating to protocol design, prototyping, experimental evaluation, real testbeds and empirical characterization of security and privacy interoperability issues in healthcare applications Explores and illustrates the current and future impacts of pandemics and mitigates risk in healthcare with advanced analytics This book is intended for students, researchers, professionals and policy makers working in the fields of public health and in the healthcare sector. Scientists and IT specialists will also find this book beneficial for research exposure and new ideas in the field of machine learning and deep learning.