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Data Science for Effective Healthcare Systems [Pehme köide]

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Edited by (PIET), Edited by (JUIT), Edited by (JUIT), Edited by (JUIT)
  • Formaat: Paperback / softback, 214 pages, kõrgus x laius: 254x178 mm, kaal: 453 g, 34 Tables, black and white; 69 Line drawings, black and white; 25 Halftones, black and white; 94 Illustrations, black and white
  • Sari: Chapman & Hall/CRC Internet of Things
  • Ilmumisaeg: 07-Oct-2024
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 1032307501
  • ISBN-13: 9781032307503
Teised raamatud teemal:
Data Science for Effective Healthcare SystemsSuurem pilt
  • Formaat: Paperback / softback, 214 pages, kõrgus x laius: 254x178 mm, kaal: 453 g, 34 Tables, black and white; 69 Line drawings, black and white; 25 Halftones, black and white; 94 Illustrations, black and white
  • Sari: Chapman & Hall/CRC Internet of Things
  • Ilmumisaeg: 07-Oct-2024
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 1032307501
  • ISBN-13: 9781032307503
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781032307503.html
Märksõnad:

Data Science for Effective Healthcare Systems has a prime focus on the importance of data science in the healthcare domain. Various applications of data science in the health care domain have been studied to find possible solutions. In this period of COVID-19 pandemic data science and allied areas plays a vital role to deal with various aspect of health care. Image processing, detection & prevention from COVID-19 virus, drug discovery, early prediction, and prevention of diseases are some thrust areas where data science has proven to be indispensable.

Key Features:

The book offers comprehensive coverage of the most essential topics, including:

  • Big Data Analytics, Applications & Challenges in Healthcare
  • Descriptive, Predictive and Prescriptive Analytics in Healthcare
  • Artificial Intelligence, Machine Learning, Deep Learning and IoT in Healthcare
  • Data Science in Covid-19, Diabetes, Coronary Heart Diseases, Breast Cancer, Brain Tumor

The aim of this book is also to provide the future scope of these technologies in the health care domain. Last but not the least, this book will surely benefit research scholar, persons associated with healthcare, faculty, research organizations, and students to get insights into these emerging technologies in the healthcare domain.



This book has a prime focus on the importance of data science in the healthcare domain. The aim of the book is to provide the future scope of these technologies in the health care domain. It will benefit research scholar, faculty, research organizations, and students to get insights into these emerging technologies in the healthcare domain.

1. Big Data in Healthcare: Applications and Challenges.
2. Impact Analysis of COVID-19 on Different Countries: A Big Data Approach.
3. Overview of Image Processing Technology in Healthcare Systems.
4. Artificial Intelligence Fight Against the Covid-19 Coronavirus in Bharat.
5. Classification Based Prediction Techniques Using ML: A Perspective for Health Care.
6. Deep Learning for Drug Discovery: Challenges and Opportunities.
7. Issues and Challenges Associated with Machine Learning Tools for Health Care System.
8. Real-Time Data Analysis on Covid-19 Vaccination Progress over the World.
9. Descriptive, Predictive, and Prescriptive Analytics in Healthcare.
10. IoT Enabled Worker Health, Safety Monitoring and Visual Data Analytics.
11. Prevalence of Nomophobia and Its Association with Text Neck Syndrome and Insomnia in Young Adults during COVID-19.
12. The Role of AI, Fuzzy Logic System in Computational Biology and Bioinformatics.
13. Analysis for Early Prediction of Diabetes in Healthcare using Classification Techniques.
14. Nomenclature of Machine learning Algorithms and Its Applications.
15. Breast Cancer Prognosis using Machine Learning Approaches.
16. Machine Learning-Based Active Contour Approach for the Recognition of Brain Tumor Progression.
17. A Deep Neural Networks-Based Cost-Effective Framework for Diabetic Retinopathy Detection.

Hari Singh; Dinesh Chander Verma