Evolving Predictive Analytics in Healthcare : New AI Techniques for Real-Time Interventions

• Record Nr.: UNINA9911007017203321
• Autore: Kumar Abhishek
• Pubbl/distr/stampa: Piraí : , : Institution of Engineering & Technology, , 2022; ©2022
• ISBN: 1-83724-475-8; 1-5231-5343-1; 1-83953-512-1
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (362 pages)
• Collana: Healthcare Technologies
• Altri autori (Persone): DubeyAshutosh Kumar; BhatiaSurbhi; KumarSwarn Avinash; LeDac-Nhuong
• Disciplina: 610.285
• Soggetti: Artificial intelligence - Medical applications; Predictive analytics
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: 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.
• Sommario/riassunto: 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.