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Medical Analytics for Clinical and Healthcare Applications

Edited by Kanak Kalita, Divya Zindani, Narayanan Ganesh and Xiao-Zhi Gao
Copyright: 2025   |   Expected Pub Date:2025/07/30
ISBN: 9781394301454  |  Hardcover  |  
350 pages

One Line Description
The book is essential for anyone exploring the forefront of healthcare innovation, as it offers a thorough exploration of transformative data-driven methodologies that can significantly enhance patient outcomes and clinical efficiency in today’s evolving medical landscape.

Audience
Healthcare professionals, clinical researchers, medical data scientists, biomedical engineers, IT professionals, academics, and policymakers focused on the intersection of medicine and data analytics

Description
In today’s rapidly advancing healthcare landscape, the integration of medical analytics has become essential for improving patient outcomes, clinical efficiency, and decision-making. Medical Analytics for Clinical and Healthcare Applications provides a comprehensive examination of how data-driven methodologies are revolutionizing the medical field. This book offers a deep dive into innovative techniques, real-world applications, and emerging trends in medical analytics, showcasing how these advancements are transforming disease detection, diagnosis, treatment planning, and healthcare management.
Spanning sixteen chapters across five subsections, this edited volume covers a wide array of topics—from foundational principles of medical data analysis to cutting-edge applications in predictive healthcare and medical data security. Readers will encounter state-of-the-art methodologies, including machine learning
models, predictive analytics, and deep learning techniques applied to various healthcare challenges such as mental health disorders, cancer detection, and hospital mortality predictions. Medical Analytics for Clinical and Healthcare Applications equips readers with the knowledge to harness the power of medical
analytics and its potential to shape the future of healthcare. Through its interdisciplinary approach and expert insights, this volume is poised to serve as a valuable resource for advancing healthcare technologies and improving the overall quality of care.
Readers will find the volume:
• Explores the latest medical analytics techniques applied across clinical settings, from diagnosis to treatment optimization;
• Features real-world case studies and tools for implementing data-driven solutions in healthcare;
• Bridges the gap between healthcare professionals, data scientists, and engineers for collaborative innovation in medical technologies;
• Provides foresight into emerging trends and technologies shaping the future of healthcare analytics.

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Author / Editor Details
Kanak Kalita, PhD is an accomplished professor and researcher in the field of computational engineering with over eight years of experience. He has published over 180 articles in international journals and edited five books. His research interests include machine learning, fuzzy decision making, metamodeling,
process optimization, finite element methods, and composites.

Divya Zindani, PhD is an assistant professor in Department of Mechanical Engineering at the Sri Sivasubramaniya Nadar College of Engineering. He has published 15 patents, 15 books, over 20 chapters, and more than 60 journal publications. His research interests include sustainable materials, optimization,
decision support systems, and supply chain management.

Narayanan Ganesh, PhD is a senior associate professor in the School of Computer Science and Engineering at the Vellore Institute of Technology with over two decades of experience. He has over 35 publications to his credit, including internationally published journal articles and book chapters. His research interests include software engineering, agile software development, prediction and optimization techniques, deep learning, image processing, and data analytics.

Xiao-Zhi Gao, PhD is a professor at the University of Eastern Finland. He has published over 400 technical papers in international journals and conferences. His research focuses on nature-inspired computing methods with applications in optimization, data mining, machine learning, control, signal processing, and
industrial electronics.

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Table of Contents
Preface
Part 1: Foundations of Medical Analytics
1. Exploring Trends in Depression and Anxiety Using Machine and Deep Learning Models

Garvit Jakar, Timothy George, Parvathi R., Pattabiraman V. and Xiaohui Yuan
1.1 Introduction
1.2 Exploratory Data Analysis
1.2.1 Exploratory Data Analysis (EDA)
1.2.2 Interactive Visualizations with Plotly
1.2.3 Data Cleaning and Transformation
1.2.4 Chart Creation with Matplotlib and Seaborn
1.2.5 Documentation and Report Writing
1.3 Problem Statement and Motivation
1.4 Literature Survey
1.5 Data Visualization
1.5.1 Integration of Machine Learning-Powered Insights
1.5.2 Dynamic Time-Series Visualizations with Plotly
1.5.3 Fusion of Art and Data with Seaborn Styling
1.5.4 Ethical Considerations in Visual Storytelling
1.5.5 Seamless Collaboration Through Git Version Control
1.6 Overview of Dataset
1.6.1 Dataset Acquisition Links
1.6.2 Household Pulse Survey
1.7 Methodology
1.7.1 Data Selection and Attributes
1.7.2 Data Pre-Processing
1.8 Modules
1.8.1 Linear Regression
1.8.2 K-Means Clustering
1.8.3 Random Forest
1.8.4 Simple Convolutional Neural Network (CNN)
1.8.5 MobileNetV2
1.8.6 Basic Visualizations
1.9 Results and Discussion
1.10 Conclusion
References
Part 2: Disease Detection and Diagnosis
2. An Innovative Framework for the Detection and Classification of Breast Cancer Disease Using Logistic Regression Compared with Back Propagation Neural Network

K. Reema Sekhar and Ashley Thomas
2.1 Introduction
2.2 Materials and Methods
2.2.1 Back Propagation Neural Network (BPNN)
2.2.2 Logistic Regression (LR)
2.2.3 Statistical Analysis
2.3 Results
2.4 Discussion
2.5 Conclusion
References
3. An Approach to Conduct the Diabetes Prediction Using AdaBoost Algorithm Compared with Decision Tree Classifier Algorithm
P. Jaswanth Reddy and R. Thalapathi Rajasekaran
3.1 Introduction
3.2 Materials and Methods
3.2.1 AdaBoost Algorithm
3.2.1.1 Procedure for AdaBoost Algorithm
3.2.1.2 Decision Tree
3.2.1.3 Statistical Analysis
3.3 Results and Discussion
3.4 Conclusion
References
4. Efficient Net V2-Based Pneumonia Detection: A Comparative Study with Transfer Learning Models
Suguna M., Shane V. Jose, Om Kumar C.U., Gunasekaran T. and Prakash D.
4.1 Introduction
4.2 Related Works
4.3 Materials and Methods
4.3.1 Dataset Details
4.3.2 CLAHE
4.3.3 Data Augmentation
4.3.4 Transfer Learning Models
4.3.4.1 VGG-16
4.3.4.2 Xception
4.3.4.3 EfficientNetV2
4.4 Results and Discussion
4.4.1 Ensemble Model
4.5 Conclusion and Future Work
References
5. A Histogram Equalized Median Filtered SIFT–EfficientNet Based on Deep Learning Approach for Lung Disease Detection
Suguna M., Pujala Shree Lekha, Om Kumar C.U., Arunmozhi M. and Prakash D.
5.1 Introduction
5.2 Related Works
5.3 Materials and Methods
5.3.1 Dataset
5.3.2 Methodology: HMS-E
5.3.3 Pre-Processing
5.3.4 Histogram Equalization
5.3.5 Median Filter
5.3.6 Feature Extraction
5.3.6.1 Detection of Scale-Space Extrema
5.3.6.2 Localization of Key Points
5.3.6.3 Generation of Key Point Descriptors
5.3.7 Deep Learning Model
5.3.7.1 EfficientNetB0
5.4 Performance Measure
5.5 Results and Discussion
5.6 Conclusion and Future Work
References
Part 3: Predictive Analytics in Healthcare
6. Comparing the Efficiency of ResNet-50 and Convolutional Neural Networks for Facial Mask Detection

Shaik Khaleel Basha and K. Nattar Kannan
6.1 Introduction
6.2 Materials and Methods
6.3 ResNet-50 Architecture
6.4 Convolutional Neural Networks (CNN)
6.5 Statistical Analysis
6.6 Results and Discussion
6.7 Conclusion
References
7. Enhancing Accuracy in Predicting Knee Osteoarthritis Progression Using Kellgren–Lawrence Grade Compared with Deep Convolutional Neural Network
Sai Srinivasa and Malarkodi K.
7.1 Introduction
7.2 Materials and Methods
7.2.1 Kellgren–Lawrence Grade
7.2.2 Deep Convolutional Neural Network
7.2.3 Statistical Analysis
7.3 Results and Discussion
7.3.1 Accuracy and Loss Analysis
7.3.2 Group Statistical Analysis
7.3.3 Independent Sample T-Test Results
7.3.4 Performance Comparison
7.3.5 Discussion
7.4 Conclusion
References
8. A Comparative Analysis of Support Vector Machine over K-Neighbors Classifier for Predicting Hospital Mortality with Improved Accuracy
Prabhu Kumar Adi and C. Anitha
8.1 Introduction
8.1.1 Integration of Present-On-Admission Indicators
8.1.2 Challenges in Implementing Machine Learning Models
8.1.3 Predicting in-Hospital Mortality in ICU Patients
8.1.4 Machine Learning Algorithms in Mortality Prediction
8.1.5 Ethical and Operational Considerations
8.2 Materials and Methods
8.2.1 Software and Hardware Configuration
8.2.2 SVM Algorithm
8.2.3 K-Nearest Neighbor Algorithm
8.2.4 Statistical Analysis
8.3 Results and Discussion
8.3.1 Accuracy and Loss Analysis
8.3.2 Independent Sample T-Test Analysis
8.3.3 Comparison of SVM and KNN
8.3.4 Real-World Application and Implications
8.3.5 Limitations and Future Scope
8.4 Conclusion
References
9. Asthma Prediction Using Vowel Inspiration: A Machine Learning Approach
Sandhya Prasad, Anik Bhaumik, Suvidha Rupesh Kumar, Rama Parvathy L., Heshalini Rajagopal and Janani S.
9.1 Introduction
9.2 Literature Survey
9.3 Motivation and Background
9.4 Proposed Method
9.4.1 Vowel Inspiration
9.4.2 Voice Activity Detection
9.4.3 Inspiration Filtering
9.4.4 Feature Extraction
9.4.4.1 Pause Frequency
9.4.4.2 Average Phonation Time
9.4.4.3 Vocalization-to-Inhalation Ratio
9.4.4.4 Inspiration Sound Energy
9.4.5 Classification
9.4.6 Praat Parameters
9.4.6.1 Jitter
9.4.6.2 NHR
9.4.6.3 HNR
9.5 Discussion
9.5.1 Classification for First Method
9.5.1.1 Logistic Regression
9.5.1.2 Naïve Bayes
9.5.1.3 K-Nearest Neighbor
9.5.1.4 Support Vector Machine
9.5.1.5 Decision Tree
9.5.1.6 Random Forest
9.5.1.7 Logistic Regression with Random Forest
9.5.2 Classification for Second Method
9.5.2.1 K-Nearest Neighbor with Classification Report
9.5.2.2 Logistic Regression with Discussion on Classification Report
9.5.2.3 Decision Tree with Discussion on Classification Report
9.5.2.4 Naïve Bayes with Discussion on Classification Report
9.6 Results
9.7 Conclusion
References
Part 4: Medical Data Analysis and Security
10. Improvement of Accuracy in Prevention of Medical Images from Security Threats Using Novel Lasso Regression in Comparison with K-Means Classifier

K. Raghul and M. Kalaiyarasi
10.1 Introduction
10.2 Materials and Methods
10.2.1 Dataset Variables
10.2.2 Machine Learning Algorithms: Novel Lasso Regression and K-Means Classifiers
10.2.2.1 Novel Lasso Regression
10.2.2.2 K-Means Classifier
10.2.3 Statistical Analysis
10.3 Result
10.4 Discussion
10.5 Conclusion
References
11. Renal Cancer Detection from Histopathological Images Using Deep Learning
Akhil Kumar, R. Krithiga, S. Suseela, B. Swarna and T. Karthikeyan
11.1 Introduction
11.1.1 Motivation
11.2 Materials and Methods
11.2.1 Dataset Used
11.2.2 Models Used
11.3 Results and Discussions
11.3.1 High Classification Accuracy
11.3.2 Strong Performance on Classification Metrics
11.3.3 Fuhrman Grade Classification
11.4 Conclusion and Future Work
References
12. A Novel Method to Predicting Tumor in Fallopian Tube Using DenseNet Over Linear Regression with Enhanced Efficiency
Harish C.M. and Terrance Frederick Fernandez
12.1 Introduction
12.2 Materials and Methods
12.2.1 DenseNet Algorithm
12.2.1.1 Pseudocode for DenseNet
12.2.2 Linear Regression
12.2.2.1 Pseudocode for Linear Regression
12.2.3 Statistical Analysis
12.3 Results and Discussion
12.3.1 Accuracy and Loss Comparison
12.3.2 Statistical Analysis Results
12.3.3 Group Statistical Analysis
12.3.4 Discussion
12.3.5 DenseNet’s Performance Advantages
12.3.6 Linear Regression’s Limitations
12.3.7 Significance of Statistical Analysis
12.3.8 Limitations and Future Work
12.4 Conclusion
References
13. Protected Medical Images Against Security Threats Using Lasso Regression and K-Means Algorithms
N. Sainath Reddy and S. Tamilselvan
13.1 Introduction
13.2 Materials and Methods
13.3 K-Means Classifier
13.4 Procedure for K-Means Classifier
13.5 Lasso Regression
13.6 Procedure for Lasso Regression
13.7 Statistical Analysis
13.8 Results
13.9 Discussion
13.10 Conclusion
References
Part 5: Emerging Trends and Technologies
14. Predicting the Factors Influencing Alcoholic Consumption of Teenagers Using an Optimized Random Forest Classifier in Comparison with Logistic Regression

Devineni Giri and M. Gunasekaran
14.1 Introduction
14.2 Materials and Methods
14.3 Random Forest Classifier
14.4 Algorithm for Random Forest Classifier
14.5 Logistic Regression Classifier
14.6 Algorithm for Logistic Regression Classifier
14.7 Results
14.8 Discussion
14.9 Conclusion
References
15. Harnessing Food Waste Potential: Advancing Protein Sequence Motif Analysis with Novel Cluster Sequence Analyzer Machine Learning Model
U. Vignesh, Geetha S. and Benson Edwin Raj
15.1 Introduction
15.1.1 Motif Regions
15.2 Suffix Tree
15.3 Clustering Algorithms in PPI
15.4 Classification Agorithms in PPI
15.5 CSA and PPI Interaction Results
15.6 Conclusion
Bibliography
16. “Hi-Tech People, Digitized HR— Are We Missing the Humane Link?”—Use of People Analytics as an Effective HRM Tool in a Selected Healthcare Sector
Rana Bandyopadhyay and Aniruddha Banerjee
16.1 Introduction
16.2 Research Background
16.3 Literature Review
16.4 Research Gaps
16.5 Research Methodology
16.6 Objectives
16.7 NH Success Story
16.8 Analysis and Discussion
16.9 Findings
16.10 People Analytics and Humane Touch
16.11 Conclusions
References
Index

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