Optimized Predictive Models in Health Care Using Machine Learning.

Format:

Book

Author/Creator:

Kumar, Sandeep.

Contributor:

Sharma, Anuj.

Nawanīta Kaura.

Pawar, Lokesh.

Bajaj, Rohit.

Language:

English

Subjects (All):

Medical statistics.

Medical technology.

Machine learning.

Artificial intelligence--Medical applications.

Artificial intelligence.

Physical Description:

1 online resource (385 pages)

Edition:

1st ed.

Place of Publication:

Newark : John Wiley & Sons, Incorporated, 2024.

Summary:

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 more about real-time applications. The book focuses on how humans and computers interact to ever-increasing levels of complexity and simplicity and provides content on the theory of optimized predictive model design, evaluation, and user diversity. Predictive modeling, a field of machine learning, has emerged as a powerful tool in healthcare for identifying high-risk patients, predicting disease progression, and optimizing treatment plans. By leveraging data from various sources, predictive models can help healthcare providers make informed decisions, resulting in better patient outcomes and reduced costs. Other essential features of the book include: provides detailed guidance on data collection and preprocessing, emphasizing the importance of collecting accurate and reliable data; explains how to transform raw data into meaningful features that can be used to improve the accuracy of predictive models; gives a detailed overview of machine learning algorithms for predictive modeling in healthcare, discussing the pros and cons of different algorithms and how to choose the best one for a specific application; emphasizes validating and evaluating predictive models; provides a comprehensive overview of validation and evaluation techniques and how to evaluate the performance of predictive models using a range of metrics; discusses the challenges and limitations of predictive modeling in healthcare; highlights the ethical and legal considerations that must be considered when developing predictive models and the potential biases that can arise in those models. Audience The book will be read by a wide range of professionals who are involved in healthcare, data science, and machine learning.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Index.

Notes:

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Kumar, Sandeep Optimized Predictive Models in Health Care Using Machine Learning

ISBN:

9781394175376

139417537X

9781394175369

1394175361

OCLC:

1425141783