Evolving predictive analytics in healthcare : new AI techniques for real-time interventions /

This book examines machine learning trends in predictive technology to solve real-time healthcare problems. By using real-time data inputs to build predictive models, this new technology can model disease progression, assist with interventions or predict patient outcomes.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Kumar, Abhishek, 1989- (Editor ), Dubey, Ashutosh Kumar (Editor ), Bhatia, Surbhi, 1988- (Editor ), Kumar, Swarn Avinash (Editor ), Le, Dac-Nhuong, 1983- (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : The Institution of Engineering and Technology, 2022.
Colección:Healthcare technologies series ; 43.
Temas:
Artificial intelligence > Medical applications.
Predictive analytics.
Intelligence artificielle en médecine.
Artificial intelligence > Medical applications
Predictive analytics
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro
  • Title
  • Copyright
  • Contents
  • About the Editors
  • 1 COVID-19 detection in X-ray images using customized CNN model
  • 1.1 Introduction
  • 1.2 Related work
  • 1.2.1 Key contributions and proposed work
  • 1.3 Materials and methods
  • 1.3.1 Feature extraction and selection
  • 1.4 Results and discussion
  • 1.5 Conclusion and future scope
  • References
  • 2 Introducing deep learning in medical diagnosis
  • 2.1 Introduction
  • 2.2 Literature survey
  • 2.3 Overview of DL algorithms
  • 2.3.1 Convolutional neural network
  • 2.3.2 Recurrent neural network
  • 2.3.3 Long short-term memory
  • 2.3.4 Restricted Boltzmann machine
  • 2.3.5 Deep belief networks
  • 2.4 Proposed DL framework for neuro disease diagnosis
  • 2.4.1 FAST-RCNN
  • 2.4.2 Ten fully connected layer
  • 2.5 Preprocessing of dataset
  • 2.6 Implementation and results
  • 2.7 Conclusion
  • References
  • 3 Intelligent approach for network intrusion detection system (NIDS) utilizing machine learning (ML)
  • 3.1 Introduction
  • 3.1.1 DoS and DDoS attacks
  • 3.1.2 Man-in-the-middle (MitM) attack
  • 3.1.3 Phishing and spear-phishing attacks
  • 3.1.4 Password attack
  • 3.1.5 Eavesdropping attack
  • 3.1.6 Malware attack
  • 3.2 Related work
  • 3.3 Cloud computing
  • 3.3.1 Machine learning
  • 3.3.2 Exploratory data analysis
  • 3.4 Results
  • References
  • 4 Classification methodologies in healthcare
  • 4.1 Introduction
  • 4.2 Classification algorithms
  • 4.2.1 Statistical data
  • 4.2.2 Discriminant analysis
  • 4.2.3 Decision tree
  • 4.2.4 K-nearest neighbor (KNN)
  • 4.2.5 Logistic regression (LR)
  • 4.2.6 Bayesian classifier
  • 4.2.7 Support vector machine (SVM)
  • 4.3 Parameter identification
  • 4.3.1 Feature selection for classi cation
  • 4.4 Real-time applications
  • 4.4.1 Classification of patients based on medical record
  • 4.4.2 Predictive analytics and diagnostic analytics based on medical records
  • 4.4.3 Classification of diseases based on medical imaging
  • 4.4.4 Mixed reality-based automation to help aid aging society
  • 4.4.5 Tiny ML-based classification systems for medical gadgets
  • 4.4.6 Classification systems for insurance claim management
  • 4.4.7 Case study: Inspectra from Perceptra
  • 4.4.8 Deep learning for beginners
  • References
  • 5 Introducing deep learning in medical domain
  • 5.1 Introduction
  • 5.1.1 DL in a nutshell
  • 5.1.2 History of DL in the medical field
  • 5.1.3 Benefits of DL in the medical domain
  • 5.1.4 Challenges and obstacles of DL in the medical domain
  • 5.1.5 Opportunities of DL in the medical field
  • 5.2 DL applications in the medical domain
  • 5.2.1 Drug discovery and medicine precision
  • 5.2.2 Detection of diseases
  • 5.2.3 Diagnosing patients
  • 5.2.4 Healthcare administration
  • 5.3 DL for medical image analysis
  • 5.3.1 Medical image detection
  • 5.3.2 Medical image recognition
  • 5.3.3 Medical image segmentation
  • 5.3.4 Medical image registration

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