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Artificial Intelligence and Machine Learning for Women's Health Issues

E-BookEPUBDRM AdobeE-Book
300 Seiten
Englisch
Elsevier Science & Techn.erschienen am01.05.2024
Artificial Intelligence and Machine Learning for Women's Health Issues: Challenges, Impact, and Solutions discusses the applications, challenges, and solutions that machine learning can bring to women's health challenges. This book illustrates advanced, innovative techniques, frameworks, concepts, and methodologies of machine learning, which enhance the future healthcare system. This book's primary focus is on women's health issues and machine learning's role in providing solutions to these challenges, providing novel ideas for feasible implementation. It also provides an early-stage analysis for early diagnosis of women's health issues.Provides fundamental concepts and analysis of machine learning algorithms used to aid in the diagnosis of women's health issues
Guides researchers to specific ideas, tools, and practices most applicable to product/service development, innovation problems, and opportunities
Provides hands-on chapters that describe frameworks, applications, best practices, and case studies of future directions of applied machine learning in women's healthcare
mehr
Verfügbare Formate
TaschenbuchKartoniert, Paperback
EUR173,50
E-BookEPUBDRM AdobeE-Book
EUR160,00

Produkt

KlappentextArtificial Intelligence and Machine Learning for Women's Health Issues: Challenges, Impact, and Solutions discusses the applications, challenges, and solutions that machine learning can bring to women's health challenges. This book illustrates advanced, innovative techniques, frameworks, concepts, and methodologies of machine learning, which enhance the future healthcare system. This book's primary focus is on women's health issues and machine learning's role in providing solutions to these challenges, providing novel ideas for feasible implementation. It also provides an early-stage analysis for early diagnosis of women's health issues.Provides fundamental concepts and analysis of machine learning algorithms used to aid in the diagnosis of women's health issues
Guides researchers to specific ideas, tools, and practices most applicable to product/service development, innovation problems, and opportunities
Provides hands-on chapters that describe frameworks, applications, best practices, and case studies of future directions of applied machine learning in women's healthcare
Details
Weitere ISBN/GTIN9780443218903
ProduktartE-Book
EinbandartE-Book
FormatEPUB
Format HinweisDRM Adobe
Erscheinungsjahr2024
Erscheinungsdatum01.05.2024
Seiten300 Seiten
SpracheEnglisch
Dateigrösse11506 Kbytes
Artikel-Nr.14561025
Rubriken
Genre9200

Inhalt/Kritik

Inhaltsverzeichnis
1. Role of Artificial Intelligence in Gynecology and Obstetrics
2. Prediction of Female Pregnancy Complication using Artificial Intelligence
3. Early Stage Prediction of Endometriosis Cancer Using Fuzzy Machine Learning Technique
4. Artificial Intelligence approaches for ultrasound examination in pregnancy
5. Early assessment of pregnancy using machine learning
6. Ensemble learning-based analysis of perinatal health disorders in women
7. Machine learning applications to predict gestational diabetes in early pregnancy
8. Contribution of artificial intelligence to improve women health in pregnancy
9. Artificial Intelligence based Prediction of Health Risks Among Women during Menopause
10. Mammography Screening of Women in Forties: Benefits and Risks
11. Machine learning approach to predict the early assessment of Post partum depression
12. Artificial intelligence approaches for polycystic ovarian syndrome
13. Improving women's mental health through AI-powered interventions and diagnoses
14. Early stage breast cancer diagnostics using Vision Transformers
15. Recent and Future Applications of Artificial Intelligence in Obstetric Ultrasound Examination
16. Deadly Canker of Cervix Tackled With Early Diagnosis using Machine Learning
17. AI, Women's health care and Trust: Problems and Prospects
18. Role of Artificial Intelligence and Machine learning in women's health: Challenges and Solutions
mehr

Autor