| Description |
1 online resource (385 pages) |
| Contents |
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 |
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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 |
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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 |
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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 |
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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 & -- Conclusions -- References -- Index |
| Notes |
Description based on publisher supplied metadata and other sources |
| Form |
Electronic book
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| Author |
Sharma, Anuj
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Kaur, Navneet
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Pawar, Lokesh
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Bajaj, Rohit
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| ISBN |
1394175361 |
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9781394175369 |
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