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Hardcover. Condición: Brand New. 408 pages. 9.18x6.12x9.45 inches. In Stock.

Hardcover. Condición: new. Hardcover. This book explores the latest advancements in machine learning techniques and their transformative applications in the healthcare domain. It delves into the use of machine learning for disease diagnosis and prognosis, showcasing its potential to enable accurate disease identification, effective risk stratification, and personalized treatment planning. The role of machine learning in enhancing clinical decision support systems (CDSS) is examined in detail, with a focus on its impact on informed decisionmaking, predictive modelling, and realtime patient monitoring.Features realworld case studies and applications that demonstrate the practical use of machine learning in healthcare, including radiology, predictive analytics, personalised medicine, and resource optimisationCovers essential stages of data preprocessing and feature engineering for healthcare datasets, addressing challenges such as data cleaning, normalisation, dimensionality reduction, and feature selectionProvides an indepth overview of CDSS and the integration of machine learning algorithms to improve diagnostic accuracy and clinical workflow efficiencyExplores machine learningdriven realtime monitoring and alert systems, underscoring their utility in promptly identifying and responding to critical medical eventsDiscusses advances in medical image analysis, including segmentation, classification, and computeraided diagnosis techniquesThis comprehensive volume serves as a valuable resource for researchers, clinicians, healthcare professionals, data scientists, and students seeking to understand and apply machine learning for improved healthcare outcomes. This book explores the latest advancements in machine learning techniques and their transformative applications in the healthcare domain. It delves into the use of machine learning for disease diagnosis and prognosis, showcasing its potential to enable accurate disease identification, effective risk stratification, and treatment planning. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condición: new. Hardcover. This book explores the latest advancements in machine learning techniques and their transformative applications in the healthcare domain. It delves into the use of machine learning for disease diagnosis and prognosis, showcasing its potential to enable accurate disease identification, effective risk stratification, and personalized treatment planning. The role of machine learning in enhancing clinical decision support systems (CDSS) is examined in detail, with a focus on its impact on informed decisionmaking, predictive modelling, and realtime patient monitoring.Features realworld case studies and applications that demonstrate the practical use of machine learning in healthcare, including radiology, predictive analytics, personalised medicine, and resource optimisationCovers essential stages of data preprocessing and feature engineering for healthcare datasets, addressing challenges such as data cleaning, normalisation, dimensionality reduction, and feature selectionProvides an indepth overview of CDSS and the integration of machine learning algorithms to improve diagnostic accuracy and clinical workflow efficiencyExplores machine learningdriven realtime monitoring and alert systems, underscoring their utility in promptly identifying and responding to critical medical eventsDiscusses advances in medical image analysis, including segmentation, classification, and computeraided diagnosis techniquesThis comprehensive volume serves as a valuable resource for researchers, clinicians, healthcare professionals, data scientists, and students seeking to understand and apply machine learning for improved healthcare outcomes. This book explores the latest advancements in machine learning techniques and their transformative applications in the healthcare domain. It delves into the use of machine learning for disease diagnosis and prognosis, showcasing its potential to enable accurate disease identification, effective risk stratification, and treatment planning. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

HRD. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

HRD. Condición: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Dattatray G. Takale is an assistant professor in the Department of Computer Engineering at Vishwakarma Institute of Information Technology, Pune, India. Dr. Takale earned his Ph.D. in computer science and engineering. He has over 12 years of teach.

Buch. Condición: Neu. Applied Machine Learning in Healthcare | Case-Based Approach | Dattatray G. Takale (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2025 | Chapman and Hall/CRC | EAN 9781032765945 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book explores the latest advancements in machine learning techniques and their transformative applications in the healthcare domain. It delves into the use of machine learning for disease diagnosis and prognosis, showcasing its potential to enable accurate disease identification, effective risk stratification, and treatment planning.

Hardcover. Condición: new. Hardcover. This book explores the latest advancements in machine learning techniques and their transformative applications in the healthcare domain. It delves into the use of machine learning for disease diagnosis and prognosis, showcasing its potential to enable accurate disease identification, effective risk stratification, and personalized treatment planning. The role of machine learning in enhancing clinical decision support systems (CDSS) is examined in detail, with a focus on its impact on informed decisionmaking, predictive modelling, and realtime patient monitoring.Features realworld case studies and applications that demonstrate the practical use of machine learning in healthcare, including radiology, predictive analytics, personalised medicine, and resource optimisationCovers essential stages of data preprocessing and feature engineering for healthcare datasets, addressing challenges such as data cleaning, normalisation, dimensionality reduction, and feature selectionProvides an indepth overview of CDSS and the integration of machine learning algorithms to improve diagnostic accuracy and clinical workflow efficiencyExplores machine learningdriven realtime monitoring and alert systems, underscoring their utility in promptly identifying and responding to critical medical eventsDiscusses advances in medical image analysis, including segmentation, classification, and computeraided diagnosis techniquesThis comprehensive volume serves as a valuable resource for researchers, clinicians, healthcare professionals, data scientists, and students seeking to understand and apply machine learning for improved healthcare outcomes. This book explores the latest advancements in machine learning techniques and their transformative applications in the healthcare domain. It delves into the use of machine learning for disease diagnosis and prognosis, showcasing its potential to enable accurate disease identification, effective risk stratification, and treatment planning. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.