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Optimized Predictive Models in Health Care Using Machine Learning

Edited by Sandeep Kumar, Anuj Sharma, Navneet Kaur, Lokesh Pawar and Rohit Bajaj
Copyright: 2024   |   Status: Published
ISBN: 9781394174621  |  Hardcover  |  
382 pages
Price: $195 USD
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One Line Description
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.

Audience
The book will be read by a wide range of professionals who are involved in healthcare, data science, and machine learning.

Description
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.

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Author / Editor Details
Sandeep Kumar, PhD, is a professor in the Department of Computer Science and Engineering, K L Deemed to be University, Vijayawada, Andhra Pradesh, India. He has been granted six patents and successfully filed another ten. He has published more than 100 research papers in various national and international journals and proceedings of reputed national and international conferences.

Anuj Sharma, PhD, is a professor at Maharshi Dayanand University, Rohtak, India. He has 19 years of teaching and administrative experience and has published more than 50 journal articles.

Navneet Kaur, PhD, is a professor in the Department of Computer Science & Engineering, Chandigarh University, India. She is the awardee of the Best Engineering College Teacher Award for Punjab State for the year 2019 and has published more than 35 research articles in reputed SCI journals and conferences.

Lokesh Pawar, PhD, is an assistant professor at Chandigarh University, India. He has filed two patents and has published multiple research articles in many SCI journals.

Rohit Bajaj, PhD, is an associate professor in the Department of Computer Science & Engineering, Chandigarh University, India. He has 12 years of teaching research experience and has published 60 papers in refereed journals and conferences.

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Table of Contents
Preface
1. Impact of Technology on Daily Food Habits and Their Effects on Health
Neha Tanwar, Sandeep Kumar and Shilpa Choudhary
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
2. Issues in Healthcare and the Role of Machine Learning in Healthcare
Nidhika Chauhan, Navneet Kaur, Kamaljit Singh Saini and Manjot Kaur
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
3. Improving Accuracy in Predicting Stress Levels of Working Women Using Convolutional Neural Networks
Purude Vaishali Narayanro, Regula Srilakshmi, M. Deepika and P. Lalitha Surya Kumari
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
4. Analysis of Smart Technologies in Healthcare
Shikha Jain, Navneet Kaur, Manisha Malhotra and Manjot Kaur
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
5. Enhanced Neural Network Ensemble Classification for the Diagnosis of Lung Cancer Disease
Thaventhiran Chandrasekar, Praveen Kumar Karunanithi, K.R. Sekar and Arka Ghosh
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
6. Feature Selection for Breast Cancer Detection
Kishan Sharda, Mandeep Singh Ramdev, Deepak Rawat and Pawan Bishnoi
6.1 Introduction
6.2 Literature Review
6.3 Design and Implementation
6.3.1 Feature Selection
6.4 Conclusion
References
7. An Optimized Feature-Based Prediction Model for Grouping the Liver Patients
Bhupender Yadav and Rohit Bajaj
7.1 Introduction
7.2 Literature Review
7.3 Proposed Methodology
7.4 Results and Discussions
7.5 Conclusion
References
8. A Robust Machine Learning Model for Breast Cancer Prediction
Rachna, Chahil Choudhary and Jatin Thakur
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
9. Revolutionizing Pneumonia Diagnosis and Prediction Through Deep Neural Networks
Abhishek Bhola and Monali Gulhane
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
10. Optimizing Prediction of Liver Disease Using Machine Learning Algorithms
Rachna, Tanish Jain, Deepak Shandilya and Shivangi Gagneja
10.1 Introduction
10.2 Related Works
10.3 Proposed Methodology
10.4 Result and Discussions
10.5 Conclusion
References
11. Optimized Ensembled Model to Predict Diabetes Using Machine Learning
Kamal, AnujKumar Sharma and Dinesh Kumar
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
12. Wearable Gait Authentication: A Framework for Secure User Identification in Healthcare
Swathi A., Swathi V., Shilpa Choudhary and Munish Kumar
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
13. NLP-Based Speech Analysis Using K-Neighbor Classifier
Renuka Arora and Rishu Bhatia
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
14. Fusion of Various Machine Learning Algorithms for Early Heart Attack Prediction
Monali Gulhane and Sandeep Kumar
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
15. Machine Learning-Based Approaches for Improving Healthcare Services and Quality of Life (QoL): Opportunities, Issues and Challenges
Pankaj Rahi, Rohit Bajaj, Sanjay P. Sood, Monika Dandotiyan and A. Anushya
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
16. Developing a Cognitive Learning and Intelligent Data Analysis-Based Framework for Early Disease Detection and Prevention in Younger Adults with Fatigue
Harish Padmanaban P. C. and Yogesh Kumar Sharma
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
17. Machine Learning Approach to Predicting Reliability in Healthcare Using Knowledge Engineering
Kialakun N. Galgal, Kamalakanta Muduli and Ashish Kumar Luhach
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
18. TPLSTM-Based Deep ANN with Feature Matching Prediction of Lung Cancer
Thaventhiran Chandrasekar, Praveen Kumar Karunanithi, A. Emily Jenifer and Inti Dhiraj
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
19. Analysis of Business Intelligence in Healthcare Using Machine Learning
Vipin Kumar, Chelsi Sen, Arpit Jain, Abhishek Jain and Anu Sharma
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
20. StressDetect: ML for Mental Stress Prediction
Himanshu Verma, Nimish Kumar, Yogesh Kumar Sharma and Pankaj Vyas
20.1 Introduction
20.2 Related Work
20.3 Materials and Methods
20.4 Results
20.5 Discussion & Conclusions
References
Index

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