Full 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.
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.
Contents
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 240
12 A Novel Method to Predicting Tumor in Fallopian Tube Using
DenseNet Over Linear Regression with Enhanced Efficiency 243
Harish C.M. and Terrance Frederick Fernandez
12.1 Introduction 244
12.2 Materials and Methods 246
12.3 Results and Discussion 250
12.4 Conclusion 257
13 Protected Medical Images Against Security Threats Using Lasso Regression and K-Means Algorithms 261
N. Sainath Reddy and S. Tamilselvan
13.1 Introduction 261
13.2 Materials and Methods 262
13.3 K-Means Classifier 263
13.4 Procedure for K-Means Classifier 263
13.5 Lasso Regression 263
13.6 Procedure for Lasso Regression 264
13.7 Statistical Analysis 264
13.8 Results 264
13.9 Discussion 266
13.10 Conclusion 267
Part 5: Emerging Trends and Technologies 271
14 Predicting the Factors Influencing Alcoholic Consumption of Teenagers Using an Optimized Random Forest Classifier in Comparison with Logistic Regression 273
Devineni Giri and M. Gunasekaran
14.1 Introduction 273
14.2 Materials and Methods 275
14.3 Random Forest Classifier 275
14.4 Algorithm for Random Forest Classifier 276
14.5 Logistic Regression Classifier 276
14.6 Algorithm for Logistic Regression Classifier 276
14.7 Results 277
14.8 Discussion 279
14.9 Conclusion 280
15 Harnessing Food Waste Potential: Advancing Protein Sequence Motif Analysis with Novel Cluster Sequence Analyzer Machine Learning Model 283
U. Vignesh, Geetha S. and Benson Edwin Raj
15.1 Introduction 284
15.2 Suffix Tree 289
15.3 Clustering Algorithms in PPI 293
15.4 Classification Agorithms in PPI 296
15.5 CSA and PPI Interaction Results 298
15.6 Conclusion 308
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 311
Rana Bandyopadhyay and Aniruddha Banerjee
16.1 Introduction 312
16.2 Research Background 313
16.3 Literature Review 313
16.4 Research Gaps 315
16.5 Research Methodology 315
16.6 Objectives 315
16.7 NH Success Story 315
16.8 Analysis and Discussion 316
16.9 Findings 321
16.10 People Analytics and Humane Touch 325
16.11 Conclusions 327
References 327
Index 329
