04926nam 22005773 450 991100701720332120231110221917.01-83724-475-81-5231-5343-11-83953-512-1(CKB)5580000000359112(MiAaPQ)EBC29423635(Au-PeEL)EBL29423635(NjHacI)995580000000359112(BIP)083443263(OCoLC)1343247510(EXLCZ)99558000000035911220220825d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierEvolving Predictive Analytics in Healthcare New AI Techniques for Real-Time Interventions1st ed.Piraí :Institution of Engineering & Technology,2022.©2022.1 online resource (362 pages)Healthcare Technologies 1-83953-511-3 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.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.Healthcare Technologies Evolving Predictive Analytics in HealthcareArtificial intelligenceMedical applicationsPredictive analyticsArtificial intelligenceMedical applications.Predictive analytics.610.285Kumar Abhishek977677Dubey Ashutosh Kumar1750701Bhatia Surbhi1225460Kumar Swarn Avinash1825087Le Dac-Nhuong1694308MiAaPQMiAaPQMiAaPQBOOK9911007017203321Evolving Predictive Analytics in Healthcare4392550UNINA