UID:
almahu_9949985019502882
Format:
1 online resource (729 pages)
Edition:
1st ed.
ISBN:
9780443240294
,
0443240299
Content:
Data Science in the Medical Field explores the integration of data science methodologies within healthcare settings, focusing on innovative technologies such as machine learning, neural networks, and biosensors. The book covers a range of topics from COVID-19 detection using convolutional neural networks to the role of predictive analytics in healthcare management. It presents various case studies and models, providing insights into the efficiency and potential of data-driven approaches in enhancing patient care and medical diagnostics. Aimed at healthcare professionals, researchers, and data scientists, the book seeks to advance knowledge and application of data science to improve healthcare outcomes.
Note:
Front Cover -- Data Science in the Medical Field -- Copyright Page -- Contents -- List of contributors -- About the authors -- Preface -- 1 PPH 4.0: a privacy-preserving health 4.0 framework with machine learning and cellular automata -- 1.1 Introduction -- 1.2 A brief survey of related technologies and past important works -- 1.2.1 Machine learning -- 1.2.2 MapReduce -- 1.2.3 Attribute-based encryption -- 1.2.4 Cellular automata -- 1.2.5 Elementary cellular automata alignment -- 1.2.6 Elementary cellular automata rules -- 1.2.6.1 Research objectives -- 1.3 Research methodology and proposed framework -- 1.3.1 Supervised approach for data dimensionality reduction -- 1.3.2 Filter methods -- 1.3.3 Correlation coefficient -- 1.3.4 Information gain -- 1.3.5 Fisher score -- 1.3.6 Mutual information -- 1.3.7 Chi-square -- 1.3.8 Wrapper methods -- 1.3.9 Forward selection approach -- 1.3.10 Backward elimination approach -- 1.3.11 Embedded methods -- 1.3.12 Feature transformation
Additional Edition:
ISBN 9780443240287
Additional Edition:
ISBN 0443240280
Language:
English
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