Machine Learning in Healthcare Informatics [electronic resource] / edited by Sumeet Dua, U. Rajendra Acharya, Prerna Dua.

Contributor(s):

Series: Intelligent Systems Reference Library ; 56Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2014Description: XII, 332 p. 119 illus., 50 illus. in color. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783642400179

Subject(s):

Genre/Form:

Electronic books.

Additional physical formats: Printed edition:: No titleDDC classification:

006.3 23

LOC classification:

Q342

Online resources:

Click here to access online

Contents:

From the Contents -- Introduction to Machine Learning in Healthcare Informatics -- Wavelet-Based Machine Learning Techniques for ECG Signal Analysis -- Application of Fuzzy Logic Control for Regulation of Glucose Level of Diabetic Patient -- A Study on Machine Learning in EEG Signal Analysis.

In: Springer eBooksSummary: The book is a unique effort to represent a variety of techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. The book provides a unique compendium of current and emerging machine learning paradigms for healthcare informatics and reflects the diversity, complexity and the depth and breath of this multi-disciplinary area. The integrated, panoramic view of data and machine learning techniques can provide an opportunity for novel clinical insights and discoveries.

Item type:

eBooks

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The book is a unique effort to represent a variety of techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. The book provides a unique compendium of current and emerging machine learning paradigms for healthcare informatics and reflects the diversity, complexity and the depth and breath of this multi-disciplinary area. The integrated, panoramic view of data and machine learning techniques can provide an opportunity for novel clinical insights and discoveries.