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

Contributor(s): Dua, Sumeet [editor.] | Acharya, U. Rajendra [editor.] | Dua, Prerna [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Intelligent Systems Reference Library: 56Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2014Description: XII, 332 p. 119 illus., 50 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642400179.Subject(s): Engineering | Artificial intelligence | Computational intelligence | Engineering | Computational Intelligence | Artificial Intelligence (incl. Robotics)Additional physical formats: Printed edition:: No titleDDC classification: 006.3 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.
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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.

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.

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