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Machine Learning with Health Care Perspective

Machine Learning and Healthcare

  • Provides a unique compendium of current and emerging machine learning paradigms for healthcare informatics
  • Reflects the diversity, complexity, and the depth and breadth of this multi-disciplinary area
  • Offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare challenges
  • Presents a variety of techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics

Part of the book series: Learning and Analytics in Intelligent Systems (LAIS, volume 13)

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Table of contents (16 chapters)

  1. Front Matter

    Pages i-x
  2. Machine Learning for Healthcare: Introduction

    • Shiwani Gupta, R. R. Sedamkar
    Pages 1-25
  3. Artificial Intelligence in Medical Diagnosis: Methods, Algorithms and Applications

    • J. H. Kamdar, J. Jeba Praba, John J. Georrge
    Pages 27-37
  4. Machine Learning for Healthcare Diagnostics

    • K. Kalaiselvi, M. Deepika
    Pages 91-105
  5. Disease Detection System (DDS) Using Machine Learning Technique

    • Sumana De, Baisakhi Chakraborty
    Pages 107-132
  6. Deep Learning Solutions for Skin Cancer Detection and Diagnosis

    • Hardik Nahata, Satya P. Singh
    Pages 159-182
  7. Medical Text and Image Processing: Applications, Issues and Challenges

    • Shweta Agrawal, Sanjiv Kumar Jain
    Pages 237-262
  8. Machine Learning Methods for Managing Parkinson’s Disease

    • Kunjan Vyas, Shubhendu Vyas, Nikunj Rajyaguru
    Pages 263-294
  9. Clinical Decision Support Systems and Predictive Analytics

    • Ravi Lourdusamy, Xavierlal J. Mattam
    Pages 317-355
  10. Yajna and Mantra Science Bringing Health and Comfort to Indo-Asian Public: A Healthcare 4.0 Approach and Computational Study

    • Rohit Rastogi, Mamta Saxena, Muskan Maheshwari, Priyanshi Garg, Muskan Gupta, Rajat Shrivastava et al.
    Pages 357-390
  11. Identifying Diseases and Diagnosis Using Machine Learning

    • K. Kalaiselvi, D. Karthika
    Pages 391-415

About this book

This unique book introduces a variety of techniques designed to represent, enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. Providing a unique compendium of current and emerging machine learning paradigms for healthcare informatics, it reflects the diversity, complexity, and the depth and breadth of this multi-disciplinary area. Further, it describes techniques for applying machine learning within organizations and explains how to evaluate the efficacy, suitability, and efficiency of such applications. Featuring illustrative case studies, including how chronic disease is being redefined through patient-led data learning, the book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare challenges.


Editors and Affiliations

  • Bharati Vidyapeeth's Institute of Computer Applications and Management, New Delhi, India

    Vishal Jain

  • Lord Buddha Education Foundation, Kathmandu, Nepal

    Jyotir Moy Chatterjee

Bibliographic Information

Buy it now

Buying options

eBook USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access