Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications

This text covers the fundamentals of Machine Learning and Deep Learning in healthcare, discusses data collection approaches, integrates AI-based Computational Intelligence, explores the impacts of pandemics and risk mitigation, and emphasizes feature selection as an important step in accurate model simulation.

Format: Hardback
Length: 280 pages
Publication date: 25 February 2022
Publisher: Taylor & Francis Ltd

Machine Learning and Deep Learning play pivotal roles in the realm of healthcare applications, offering immense potential for improving patient outcomes and advancing medical research. In this comprehensive guide, we delve into the fundamentals of these powerful technologies, exploring their applications in healthcare and their significance in addressing healthcare challenges.

Data collection is a crucial aspect of Machine Learning and Deep Learning models. We examine various data collection approaches, including electronic health records (EHRs), wearable devices, and social media, and discuss how these data can be utilized to train machine learning algorithms. We also explore the challenges associated with data collection, such as privacy concerns, data quality, and bias.

Integrating multiple aspects of AI-based Computational Intelligence, such as Machine Learning and Deep Learning, provides a holistic understanding of these technologies. We examine these technologies from diverse perspectives, highlighting recent research trends and advanced topics in the field. This includes exploring supervised learning, unsupervised learning, and reinforcement learning, as well as their applications in healthcare domains such as diagnosis, prediction, and drug discovery.

The impact of pandemics on healthcare has been profound, and advanced analytics plays a vital role in mitigating these risks. We explore the current and future impacts of pandemics on healthcare systems, including the challenges faced by healthcare providers, patients, and researchers. We also discuss the role of advanced analytics in predicting disease outbreaks, developing effective vaccines and treatments, and improving patient care.

Feature selection is an essential step in any accurate model simulation, and ML/DL methods are used to help train the system and extract the positive solution implicitly. We emphasize the importance of feature selection in improving the performance of machine learning models and discuss various techniques, such as feature importance analysis, dimensionality reduction, and ensemble modeling.

In conclusion, Machine Learning and Deep Learning have the potential to revolutionize healthcare by enabling personalized medicine, improving disease diagnosis and prediction, and enhancing patient care. This comprehensive guide provides an in-depth exploration of these technologies, their applications, and their significance in addressing healthcare challenges. By leveraging the power of advanced analytics and integrating diverse perspectives, we can unlock the full potential of Machine Learning and Deep Learning in healthcare and drive positive change in the industry.

Weight: 576g
Dimension: 159 x 243 x 24 (mm)
ISBN-13: 9781032126876