Autorentext

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.

Klappentext

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

  Inhalt

  Preface xv

  Part 1: Foundations of Medical Analytics 1

  1 Exploring Trends in Depression and Anxiety Using Machine and Deep Learning Models 3
  Garvit Jakar, Timothy George, Parvathi R., Pattabiraman V. and Xiaohui Yuan

  1.1 Introduction 4

  1.2 Exploratory Data Analysis 6

  1.3 Problem Statement and Motivation 7

  1.4 Literature Survey 8

  1.5 Data Visualization 9

  1.6 Overview of Dataset 10

  1.7 Methodology 13

  1.8 Modules 15

  1.9 Results and Discussion 26

  1.10 Conclusion 28

  Part 2: Disease Detection and Diagnosis 31

  2 An Innovative Framework for the Detection and Classification of Breast Cancer Disease Using Logistic Regression Compared with Back Propagation Neural Network 33
  K. Reema Sekhar and Ashley Thomas

  2.1 Introduction 34

  2.2 Materials and Methods 36

  2.3 Results 39

  2.4 Discussion 42

  2.5 Conclusion 45

  3 An Approach to Conduct the Diabetes Prediction Using AdaBoost Algorithm Compared with Decision Tree Classifier Algorithm 49
  P. Jaswanth Reddy and R. Thalapathi Rajasekaran

  3.1 Introduction 50

  3.2 Materials and Methods 53

  3.3 Results and Discussion 55

  3.4 Conclusion 61

  4 Efficient Net V2-Based Pneumonia Detection: A Comparative Study with Transfer Learning Models 65
  Suguna M., Shane V. Jose, Om Kumar C.U., Gunasekaran T. and Prakash D.

  4.1 Introduction 66

  4.2 Related Works 67

  4.3 Materials and Methods 71

  4.4 Results and Discussion 79

  4.5 Conclusion and Future Work 90

  5 A Histogram Equalized Median Filtered SIFT-EfficientNet Based on Deep Learning Approach for Lung Disease Detection 93
  Suguna M., Pujala Shree Lekha, Om Kumar C.U., Arunmozhi M. and Prakash D.

  5.1 Introduction 94

  5.2 Related Works 96

  5.3 Materials and Methods 98

  5.4 Performance Measure 112

  5.5 Results and Discussion 113

  5.6 Conclusion and Future Work 119

  Part 3: Predictive Analytics in Healthcare 125

  6 Comparing the Efficiency of ResNet-50 and Convolutional Neural Networks for Facial Mask Detection 127
  Shaik Khaleel Basha and K. Nattar Kannan

  6.1 Introduction 128

  6.2 Materials and Methods 131

  6.3 ResNet-50 Architecture 132

  6.4 Convolutional Neural Networks (CNN) 133

  6.5 Statistical Analysis 134

  6.6 Results and Discussion 135

  6.7 Conclusion 142

  7 Enhancing Accuracy in Predicting Knee Osteoarthritis Progression Using Kellgren-Lawrence Grade Compared with Deep Convolutional Neural Network 145
  Sai Srinivasa and Malarkodi K.

  7.1 Introduction 146

  7.2 Materials and Methods 149

  7.3 Results and Discussion 153

  7.4 Conclusion 158

  8 A Comparative Analysis of Support Vector Machine over K-Neighbors Classifier for Predicting Hospital Mortality with Improved Accuracy 161
  Prabhu Kumar Adi and C. Anitha

  8.1 Introduction 162

  8.2 Materials and Methods 166

  8.3 Results and Discussion 170

  8.4 Conclusion 175

  9 Asthma Prediction Using Vowel Inspiration: A Machine Learning Approach 179
  Sandhya Prasad, Anik Bhaumik, Suvidha Rupesh Kumar, Rama Parvathy L., Heshalini Rajagopal and Janani S.

  9.1 Introduction 180

  9.2 Literature Survey 182

  9.3 Motivation and Background 185

  9.4 Proposed Method 186

  9.5 Discussion 194

  9.6 Results 200

  9.7 Conclusion 202

  Part 4: Medical Data Analysis and Security 207

  10 Improvement of Accuracy in Prevention of Medical Images from Security Threats Using Novel Lasso Regression in Comparison with K-Means Classifier 209
  K. Raghul and M. Kalaiyarasi

  10.1 Introduction 210

  10.2 Materials and Methods 213

  10.3 Result 216

  10.4 Discussion 220

  10.5 Conclusion 221

  11 Renal Cancer Detection from Histopathological Images Using Deep Learning 225
  Akhil Kumar, R. Krithiga, S. Suseela, B. Swarna and T. Karthikeyan

  11.1 Introduction 226

  11.2 Materials and Methods 229

  11.3 Results and Discussions 237

  11.4 Conclusion and Future Work …