Disease Prediction Using Machine Learning Deep Learning And Data Analytics
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Disease Prediction Using Machine Learning Deep Learning And Data Analytics
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Author : Geeta Rani
language : en
Publisher: Bentham Science Publishers
Release Date : 2024-03-07
Disease Prediction Using Machine Learning Deep Learning And Data Analytics written by Geeta Rani and has been published by Bentham Science Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-07 with Computers categories.
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.
Disease Prediction Using Machine Learning Deep Learning And Data Analytics
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Author : Geeta Rani
language : en
Publisher:
Release Date : 2024
Disease Prediction Using Machine Learning Deep Learning And Data Analytics written by Geeta Rani and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with Deep learning (Machine learning) categories.
Machine Learning And Data Analytics For Predicting Managing And Monitoring Disease
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Author : Roy, Manikant
language : en
Publisher: IGI Global
Release Date : 2021-06-25
Machine Learning And Data Analytics For Predicting Managing And Monitoring Disease written by Roy, Manikant and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-25 with Computers categories.
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.
Handbook Of Deep Learning Models For Healthcare Data Processing
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Author : Ajay Kumar
language : en
Publisher: CRC Press
Release Date : 2025-06-30
Handbook Of Deep Learning Models For Healthcare Data Processing written by Ajay Kumar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-30 with Technology & Engineering categories.
In recent years, deep learning has shown great potential in transforming various fields including healthcare. With the abundance of healthcare data being generated every day, there is a pressing need to develop efficient algorithms that can process and analyze this data to improve patient care and treatment outcomes. Handbook of Deep Learning Models for Healthcare Data Processing: Disease Prediction, Analysis, and Applications covers a wide range of deep learning models, techniques, and applications in healthcare data processing, analysis, and disease prediction, providing a comprehensive overview of the field. It focuses on the practical application of deep learning models in healthcare and offers step-by-step instructions for building and deploying models and using real-world examples. The handbook discusses the potential future applications of deep learning models in healthcare, such as precision medicine, personalized treatment, and clinical decision support. It also addresses the ethical considerations associated with the use of deep learning models in healthcare, such as privacy, security, and bias. It provides technical details on deep learning models, including their architecture, training methods, and optimization techniques, making it useful for data scientists and researchers. Written to be a comprehensive guide for healthcare professionals, researchers, and data analysts, this handbook is an essential need for those who are interested in using deep learning models to analyze and process healthcare data. It is also suitable for those who have a basic understanding of machine learning and want to learn more about the latest advancements in deep learning in healthcare.
Handbook Of Research On Disease Prediction Through Data Analytics And Machine Learning
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Author : Rani, Geeta
language : en
Publisher: IGI Global
Release Date : 2020-10-16
Handbook Of Research On Disease Prediction Through Data Analytics And Machine Learning written by Rani, Geeta and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-16 with Medical categories.
By applying data analytics techniques and machine learning algorithms to predict disease, medical practitioners can more accurately diagnose and treat patients. However, researchers face problems in identifying suitable algorithms for pre-processing, transformations, and the integration of clinical data in a single module, as well as seeking different ways to build and evaluate models. The Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning is a pivotal reference source that explores the application of algorithms to making disease predictions through the identification of symptoms and information retrieval from images such as MRIs, ECGs, EEGs, etc. Highlighting a wide range of topics including clinical decision support systems, biomedical image analysis, and prediction models, this book is ideally designed for clinicians, physicians, programmers, computer engineers, IT specialists, data analysts, hospital administrators, researchers, academicians, and graduate and post-graduate students.
Integrative Machine Learning And Optimization Algorithms For Disease Prediction
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Author : Muniasamy, Anandhavalli
language : en
Publisher: IGI Global
Release Date : 2025-07-03
Integrative Machine Learning And Optimization Algorithms For Disease Prediction written by Muniasamy, Anandhavalli and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-03 with Medical categories.
Integrative approaches that combine machine learning (ML) and optimization algorithms rapidly transform the landscape of disease prediction and healthcare analytics. By leveraging the predictive power of ML models alongside the efficiency of optimization techniques, researchers can develop more accurate, robust, and scalable systems for early diagnosis and risk assessment. These hybrid frameworks enable the integration of diverse data sources into cohesive predictive models. The synergy between ML and optimization enhances model performance while supporting personalized medicine by tailoring predictions to individual patient profiles. Integrative methodologies hold significant promises for advancing clinical decision-making and improving health outcomes. Integrative Machine Learning and Optimization Algorithms for Disease Prediction explores the cutting-edge applications of machine learning, deep learning, and optimization algorithms in disease prediction. It examines how diverse machine learning models, from traditional algorithms to deep learning and ensemble methods, can be optimized for high-stakes clinical predictions. This book covers topics such as disease prediction, healthcare data, and mental health, and is a useful resource for computer engineers, medical professionals, academicians, researchers, and scientists.
Machine Learning And Analytics In Healthcare Systems
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Author : Himani Bansal
language : en
Publisher: CRC Press
Release Date : 2021-06-30
Machine Learning And Analytics In Healthcare Systems written by Himani Bansal and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-30 with Computers categories.
This book provides applications of machine learning in healthcare systems and seeks to close the gap between engineering and medicine. It will combine the design and problem-solving skills of engineering with health sciences, in order to advance healthcare treatment. The book will include areas such as diagnosis, monitoring, and therapy. The book will provide real-world case studies, gives a detailed exploration of applications in healthcare systems, offers multiple perspectives on a variety of disciplines, while also letting the reader know how to avoid some of the consequences of old methods with data sharing. The book can be used as a reference for practitioners, researchers and for students at basic and intermediary levels in Computer Science, Electronics and Communications.
Data Driven Analytics For Healthcare
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Author : Meghna Sharma
language : en
Publisher: CRC Press
Release Date : 2025-02-10
Data Driven Analytics For Healthcare written by Meghna Sharma and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-10 with Computers categories.
The new book highlights the application of artificial intelligence, machine learning, and deep learning techniques to diagnose medical problems. General predictive analysis methods are explained in disease detection applications along with machine learning techniques for the implementation of predictive analysis methods. The book covers object detection approaches for diseases such as pneumonia, application of monitoring and tracking the coronavirus disease, various endodontic applications for handling diagnostic abnormalities using deep learning methods, applications of healthcare for human activity recognition, and more.
Data Driven Diagnostics And Disease Prediction With Ai Optimization
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Author : Shailendra Pratap Singh
language : en
Publisher: Academic Press
Release Date : 2025-10-01
Data Driven Diagnostics And Disease Prediction With Ai Optimization written by Shailendra Pratap Singh and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-01 with Science categories.
Data-Driven Diagnostics and Disease Prediction with AI Optimization provides useful insights into model creation, data preparation, and ethical issues for healthcare applications. The book covers all the conventional and non-conventional methods related to this domain. It also discusses AI-based optimization techniques, Machine Learning models, and Advanced AI, offering practical insights, case studies, and optimization strategies to help data scientists and researchers efficiently employ AI in diagnostics and illness prediction in a world where precise diagnostics and early illness prediction may save lives and healthcare resources. - Provides a complete overview of the challenges and pain points in implementing AI-driven diagnostics and disease prediction within the healthcare industry - Explores various edge computing applications in healthcare and the use of hardware acceleration for AI applications - Presents a comprehensive resource that bridges the gap between AI and healthcare
Machine Learning For Disease Detection Prediction And Diagnosis
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Author : Tanupriya Choudhury
language : en
Publisher: Springer Nature
Release Date : 2025-07-08
Machine Learning For Disease Detection Prediction And Diagnosis written by Tanupriya Choudhury and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-08 with Medical categories.
The book “Machine Learning for Disease Detection, Prediction, and Diagnosis” can be a comprehensive guide to the novel concepts, techniques, and frameworks essential for improving the viability of existing machine-learning practices. It provides an in-depth analysis of how these new technologies are helpful to detect, predict and diagnose diseases more accurately. The book covers various topics such as image classification algorithms, supervised learning methods like support vector machines (SVM), deep neural networks (DNNs), convolutional neural networks (CNNs), etc. unsupervised approaches such as clustering algorithms as well as reinforcement learning strategies. This book is an invaluable resource for anyone interested in machine-learning applications related to disease detection or diagnosis. It explains different concepts and provides practical examples of how they can it implements using real-world data sets from medical imaging datasets or public health records databases, among others. Furthermore, it offers insights into recent advances made by researchers which have enabled automated decision-making systems based on AI models with improved accuracy over traditional methods. This text also discusses ways through which current models could improve further by incorporating domain knowledge during the model training phase, thereby increasing their efficacy even further. Overall, this book serves as a great source of information about the latest advancements made in the field of Machine Learning & Artificial Intelligence towards efficient building systems capable enough detecting & diagnosing diseases automatically while avoiding human errors resulting due manual intervention at any stage along process pipeline thus ensuring better outcomes overall. Moreover, it helps readers understand the underlying principles behind each technique discussed so that they may apply them according to their own application scenarios efficiently without worrying much about the implementation details required to get the job done the right way the first time around itself!