Optimized Predictive Models in Health Care Using Machine Learning

Optimized Predictive Models in Health Care Using Machine Learning

OPTIMIZED PREDICTIVE MODELS IN HEALTH CARE USING MACHINE LEARNING This book is a comprehensive guide to developing and implementing optimized predictive models in healthcare using machine learning and is a required resource for researchers, healthcare professionals, and students who wish to know mor...

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Detalles Bibliográficos
Autor principal: Kumar, Sandeep (-)
Otros Autores: Sharma, Anuj, Kaur, Navneet, Pawar, Lokesh, Bajaj, Rohit
Formato: Libro electrónico
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated 2024.
Edición:1st ed
Materias:
Medical statistics.
Medical technology.
Machine learning.
Artificial intelligence > Medical applications.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009811319506719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright Page
  • Contents
  • Preface
  • Chapter 1 Impact of Technology on Daily Food Habits and Their Effects on Health
  • 1.1 Introduction
  • 1.1.1 Impacts of Food on Health
  • 1.1.2 Impact of Technology on Our Eating Habits
  • 1.2 Technologies, Foodies, and Consciousness
  • 1.3 Government Programs to Encourage Healthy Choices
  • 1.4 Technology's Impact on Our Food Consumption
  • 1.5 Customized Food is the Future of Food
  • 1.6 Impact of Food Technology and Innovation on Nutrition and Health
  • 1.7 Top Prominent and Emerging Food Technology Trends
  • 1.8 Discussion
  • 1.9 Conclusions
  • References
  • Chapter 2 Issues in Healthcare and the Role of Machine Learning in Healthcare
  • 2.1 Introduction
  • 2.2 Issues in Healthcare
  • 2.2.1 Increase in Volume of Data
  • 2.2.1.1 Data Management
  • 2.2.1.2 Economic Difficulties
  • 2.2.2 Data Privacy Issues
  • 2.2.2.1 Cyber Attack and Hacking
  • 2.2.2.2 Data Sharing Trust in the Third Party
  • 2.2.2.3 Data Breaching
  • 2.2.2.4 Lack of Policy and Constitutional Limitations
  • 2.2.2.5 Doctor-Patient Relationship
  • 2.2.2.6 Data Storage and Management
  • 2.2.3 Disease-Centric Database
  • 2.2.4 Data Utilization
  • 2.2.5 Lack of Technology and Infrastructure
  • 2.3 Factors Affecting the Health
  • 2.4 Machine Learning in Healthcare
  • 2.4.1 Clinical Decision Support Systems in Healthcare
  • 2.4.2 Use of Machine Learning in Public Health
  • 2.5 Conclusion
  • References
  • Chapter 3 Improving Accuracy in Predicting Stress Levels of Working Women Using Convolutional Neural Networks
  • 3.1 Introduction
  • 3.2 Literature Survey
  • 3.3 Proposed Methodology
  • 3.3.1 Pre-Processing of Data
  • 3.3.2 Features Extraction
  • 3.3.3 Selection of Features
  • 3.3.4 Classification
  • 3.4 Result and Discussion
  • 3.5 Conclusion and Future Scope
  • References.
  • Chapter 4 Analysis of Smart Technologies in Healthcare
  • 4.1 Introduction
  • 4.2 Emerging Technologies in Healthcare
  • 4.2.1 Internet of Things
  • 4.2.2 Blockchain
  • 4.2.3 Machine Learning
  • 4.2.4 Deep Learning
  • 4.2.5 Federated Learning
  • 4.3 Literature Review
  • 4.4 Risks and Challenges
  • 4.5 Conclusion
  • References
  • Chapter 5 Enhanced Neural Network Ensemble Classification for the Diagnosis of Lung Cancer Disease
  • 5.1 Introduction
  • 5.2 Algorithm for Classification of Proposed Weight-Optimized Neural Network Ensembles
  • 5.2.1 Enhanced Raphson's Most Likelihood and Minimum Redundancy Preprocessing
  • 5.2.2 Maximum Likelihood Boosting in a Weighted Optimized Neural Network
  • 5.3 Experimental Work and Results
  • 5.4 Conclusion
  • References
  • Chapter 6 Feature Selection for Breast Cancer Detection
  • 6.1 Introduction
  • 6.2 Literature Review
  • 6.3 Design and Implementation
  • 6.3.1 Feature Selection
  • 6.4 Conclusion
  • References
  • Chapter 7 An Optimized Feature-Based Prediction Model for Grouping the Liver Patients
  • 7.1 Introduction
  • 7.2 Literature Review
  • 7.3 Proposed Methodology
  • 7.4 Results and Discussions
  • 7.5 Conclusion
  • References
  • Chapter 8 A Robust Machine Learning Model for Breast Cancer Prediction
  • 8.1 Introduction
  • 8.2 Literature Review
  • 8.2.1 Comparative Analysis
  • 8.3 Proposed Mythology
  • 8.4 Result and Discussion
  • 8.4.1 Accuracy
  • 8.4.2 Error
  • 8.4.3 TP Rate
  • 8.4.4 FP Rate
  • 8.4.5 F-Measure
  • 8.5 Concluding Remarks and Future Scope
  • References
  • Chapter 9 Revolutionizing Pneumonia Diagnosis and Prediction Through Deep Neural Networks
  • 9.1 Introduction
  • 9.2 Literature Work
  • 9.3 Proposed Section
  • 9.3.1 Input Image
  • 9.3.2 Pre-Processing
  • 9.3.3 Identification and Classification Using ResNet50
  • 9.4 Result Analysis
  • 9.5 Conclusion and Future Scope
  • References.
  • Chapter 10 Optimizing Prediction of Liver Disease Using Machine Learning Algorithms
  • 10.1 Introduction
  • 10.2 Related Works
  • 10.3 Proposed Methodology
  • 10.4 Result and Discussions
  • 10.5 Conclusion
  • References
  • Chapter 11 Optimized Ensembled Model to Predict Diabetes Using Machine Learning
  • 11.1 Introduction
  • 11.2 Literature Review
  • 11.3 Proposed Methodology
  • 11.3.1 Missing Value Imputation (MVI)
  • 11.3.2 Feature Selection
  • 11.3.3 K-Fold Cross-Validation
  • 11.3.4 ML Classifiers
  • 11.3.5 Evaluation Metrics
  • 11.4 Results and Discussion
  • 11.5 Concluding Remarks and Future Scope
  • References
  • Chapter 12 Wearable Gait Authentication: A Framework for Secure User Identification in Healthcare
  • 12.1 Introduction
  • 12.2 Literature Survey
  • 12.3 Proposed System
  • 12.3.1 Walking Detection
  • 12.3.2 Experimental Setup
  • 12.4 Results and Discussion
  • 12.4.1 Dataset Used
  • 12.4.2 Results
  • 12.4.3 Comparison Used Techniques
  • 12.5 Conclusion and Future Scope
  • References
  • Chapter 13 NLP-Based Speech Analysis Using K-Neighbor Classifier
  • 13.1 Introduction
  • 13.2 Supervised Machine Learning for NLP and Text Analytics
  • 13.2.1 Categorization and Classification
  • 13.3 Unsupervised Machine Learning for NLP and Text Analytics
  • 13.4 Experiments and Results
  • 13.5 Conclusion
  • References
  • Chapter 14 Fusion of Various Machine Learning Algorithms for Early Heart Attack Prediction
  • 14.1 Introduction
  • 14.2 Literature Review
  • 14.3 Materials and Methods
  • 14.3.1 Dataset
  • 14.3.2 EDA
  • 14.3.3 Machine Learning Model Implemented
  • 14.4 Result Analysis
  • 14.5 Conclusion
  • References
  • Chapter 15 Machine Learning-Based Approaches for Improving Healthcare Services and Quality of Life (QoL): Opportunities, Issues and Challenges
  • 15.1 Introduction.
  • 15.2 Core Areas of Deep Learning and ML-Modeling in Medical Healthcare
  • 15.3 Use Cases of Machine Learning Modelling in Healthcare Informatics
  • 15.3.1 Breast Cancer Detection Using Machine Learning
  • 15.3.2 COVID-19 Disease Detection Modelling Using Chest X-Ray Images with Machine and Transfer Learning Framework
  • 15.4 Improving the Quality of Services During the Diagnosing and Treatment Processes of Chronicle Diseases
  • 15.4.1 Evolution of New Diagnosing Methods and Tools
  • 15.4.2 Improving Medical Care
  • 15.4.3 Visualization of Biomedical Data
  • 15.4.4 Improved Diagnosis and Disease Identification
  • 15.4.5 More Accurate Health Records
  • 15.4.6 Ethics of Machine Learning in Healthcare
  • 15.5 Limitations and Challenges of ML, DL Modelling in Healthcare Systems
  • 15.5.1 Dealing With the Shortage of Knowledgeable-ML-Data Scientists and Engineers
  • 15.5.2 Handling of the Bias in ML Modelling of Healthcare Information
  • 15.5.3 Accuracy of Data Attenuation
  • 15.5.4 Lack of Data Quality
  • 15.5.5 Tuning of Hyper-Parameters for Improving the Modelling of Healthcare
  • 15.6 Conclusion
  • References
  • Chapter 16 Developing a Cognitive Learning and Intelligent Data Analysis-Based Framework for Early Disease Detection and Prevention in Younger Adults with Fatigue
  • 16.1 Introduction
  • 16.2 Proposed Framework "Cognitive-Intelligent Fatigue Detection and Prevention Framework (CIFDPF)"
  • 16.2.1 Framework Components
  • 16.2.2 Learning Module
  • 16.2.3 System Design
  • 16.2.4 Tools and Usage
  • 16.2.5 Architecture
  • 16.2.6 Architecture of CNN-RNN
  • 16.2.7 Fatigue Detection Methods and Techniques
  • 16.3 Potential Impact
  • 16.3.1 Claims for the Accurate Detection of Fatigue
  • 16.3.2 Similar Study and Results Analysis
  • 16.3.3 Application and Results
  • 16.4 Discussion and Limitations
  • 16.5 Future Work.
  • 16.5.1 Incorporation of More Physiological Signals
  • 16.5.2 Long-Term Monitoring of Fatigue in Real-World Scenarios
  • 16.5.3 Integration with Wearable Devices for Continuous Monitoring
  • 16.6 Conclusion
  • References
  • Chapter 17 Machine Learning Approach to Predicting Reliability in Healthcare Using Knowledge Engineering
  • 17.1 Introduction
  • 17.2 Literature Review
  • 17.3 Proposed Methodology
  • 17.3.1 Data Analysis (Findings)
  • 17.3.2 General Procedures
  • 17.3.3 Reviewed Algorithms
  • 17.3.4 Benefits of Machine Learning
  • 17.3.5 Drawbacks of Machine Learning
  • 17.4 Implications
  • 17.4.1 Prerequisites and Considerations
  • 17.4.2 Implementation Strategy
  • 17.4.3 Recommendations
  • 17.5 Conclusion
  • 17.6 Limitations and Scope of Future Work
  • References
  • Chapter 18 TPLSTM-Based Deep ANN with Feature Matching Prediction of Lung Cancer
  • 18.1 Introduction
  • 18.2 Proposed TP-LSTM-Based Neural Network with Feature Matching for Prediction of Lung Cancer
  • 18.3 Experimental Work and Comparison Analysis
  • 18.4 Conclusion
  • References
  • Chapter 19 Analysis of Business Intelligence in Healthcare Using Machine Learning
  • 19.1 Introduction
  • 19.2 Data Gathering
  • 19.2.1 Data Integration
  • 19.2.2 Data Storage
  • 19.2.3 Data Analysis
  • 19.2.4 Data Distribution
  • 19.2.5 Data-Driven Decisions on Generated Insights
  • 19.3 Literature Review
  • 19.4 Research Methodology
  • 19.5 Implementation
  • 19.6 Eligibility Criteria
  • 19.7 Results
  • 19.8 Conclusion and Future Scope
  • References
  • Chapter 20 StressDetect: ML for Mental Stress Prediction
  • 20.1 Introduction
  • 20.2 Related Work
  • 20.3 Materials and Methods
  • 20.4 Results
  • 20.5 Discussion &amp
  • Conclusions
  • References
  • Index.

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