Smart Healthcare Systems

Sinha, Adwitiya, Rathi, Megha

  • 出版商: CRC
  • 出版日期: 2019-07-31
  • 售價: $5,760
  • 貴賓價: 9.5$5,472
  • 語言: 英文
  • 頁數: 232
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 036703056X
  • ISBN-13: 9780367030568
  • 海外代購書籍(需單獨結帳)

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商品描述

About the Book

The book provides details of applying intelligent mining techniques for extracting & pre-processing medical data from various sources, for application-based healthcare research. Moreover, different datasets are used, thereby exploring real-world case studies related to medical informatics. This book would provide insight to the learners about Machine Learning, Data Analytics, and Sustainable Computing.

Salient Features of the Book

  • Exhaustive coverage of Data Analysis using R
  • Real-life healthcare models for:

    • Visually Impaired
    • Disease Diagnosis & Treatment options
    • Applications of Big Data & Deep Learning in Healthcare
    • Drug Discovery

  • Complete guide to learn the knowledge discovery process, build versatile real life healthcare applications
  • Compare & analyze recent healthcare technologies and trends

Target Audience

This book is mainly targeted at researchers, undergraduate, postgraduate students, academicians, and scholars working in the area of data science and its application to health sciences. Also, the book is beneficial for engineers who are engaged in developing actual healthcare solutions.

作者簡介

Dr. Adwitiya Sinha received PhD from Jawaharlal Nehru University (JNU), New Delhi. She is recipient of Senior Research Fellowship from CSIR, New Delhi, India & UGC Research Scholarship. Her application-based research is mainly focused on large-scale graphs, data analytics & confluence of sensor-based applications with social networking

Megha Rathi has 10 years of teaching experience. Worked on research project of Xform generator at NIC, Delhi. She has experience in Software development & worked as Project Associate at IIT Delhi. Her research areas include, Data Mining, Data Science Analytics, Health Science & Machine Learning.