Medical Analytics for Clinical and Healthcare Applications

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.

• Preface xvPart 1: Foundations of Medical Analytics 11 Exploring Trends in Depression and Anxiety Using Machine and Deep Learning Models 3Garvit Jakar, Timothy George, Parvathi R., Pattabiraman V. and Xiaohui Yuan1.1 Introduction 41.2 Exploratory Data Analysis 61.3 Problem Statement and Motivation 71.4 Literature Survey 81.5 Data Visualization 91.6 Overview of Dataset 101.7 Methodology 131.8 Modules 151.9 Results and Discussion 261.10 Conclusion 28Part 2: Disease Detection and Diagnosis 312 An Innovative Framework for the Detection and Classification of Breast Cancer Disease Using Logistic Regression Compared with Back Propagation Neural Network 33K. Reema Sekhar and Ashley Thomas2.1 Introduction 342.2 Materials and Methods 362.3 Results 392.4 Discussion 422.5 Conclusion 453 An Approach to Conduct the Diabetes Prediction Using AdaBoost Algorithm Compared with Decision Tree Classifier Algorithm 49P. Jaswanth Reddy and R. Thalapathi Rajasekaran3.1 Introduction 503.2 Materials and Methods 533.3 Results and Discussion 553.4 Conclusion 614 Efficient Net V2-Based Pneumonia Detection: A Comparative Study with Transfer Learning Models 65Suguna M., Shane V. Jose, Om Kumar C.U., Gunasekaran T. and Prakash D.4.1 Introduction 664.2 Related Works 674.3 Materials and Methods 714.4 Results and Discussion 794.5 Conclusion and Future Work 905 A Histogram Equalized Median Filtered SIFT–EfficientNet Based on Deep Learning Approach for Lung Disease Detection 93Suguna M., Pujala Shree Lekha, Om Kumar C.U., Arunmozhi M. and Prakash D.5.1 Introduction 945.2 Related Works 965.3 Materials and Methods 985.4 Performance Measure 1125.5 Results and Discussion 1135.6 Conclusion and Future Work 119Part 3: Predictive Analytics in Healthcare 1256 Comparing the Efficiency of ResNet-50 and Convolutional Neural Networks for Facial Mask Detection 127Shaik Khaleel Basha and K. Nattar Kannan6.1 Introduction 1286.2 Materials and Methods 1316.3 ResNet-50 Architecture 1326.4 Convolutional Neural Networks (CNN) 1336.5 Statistical Analysis 1346.6 Results and Discussion 1356.7 Conclusion 1427 Enhancing Accuracy in Predicting Knee Osteoarthritis Progression Using Kellgren–Lawrence Grade Compared with Deep Convolutional Neural Network 145Sai Srinivasa and Malarkodi K.7.1 Introduction 1467.2 Materials and Methods 1497.3 Results and Discussion 1537.4 Conclusion 1588 A Comparative Analysis of Support Vector Machine over K-Neighbors Classifier for Predicting Hospital Mortality with Improved Accuracy 161Prabhu Kumar Adi and C. Anitha8.1 Introduction 1628.2 Materials and Methods 1668.3 Results and Discussion 1708.4 Conclusion 1759 Asthma Prediction Using Vowel Inspiration: A Machine Learning Approach 179Sandhya Prasad, Anik Bhaumik, Suvidha Rupesh Kumar, Rama Parvathy L., Heshalini Rajagopal and Janani S.9.1 Introduction 1809.2 Literature Survey 1829.3 Motivation and Background 1859.4 Proposed Method 1869.5 Discussion 1949.6 Results 2009.7 Conclusion 202Part 4: Medical Data Analysis and Security 20710 Improvement of Accuracy in Prevention of Medical Images from Security Threats Using Novel Lasso Regression in Comparison with K-Means Classifier 209K. Raghul and M. Kalaiyarasi10.1 Introduction 21010.2 Materials and Methods 21310.3 Result 21610.4 Discussion 22010.5 Conclusion 22111 Renal Cancer Detection from Histopathological Images Using Deep Learning 225Akhil Kumar, R. Krithiga, S. Suseela, B. Swarna and T. Karthikeyan11.1 Introduction 22611.2 Materials and Methods 22911.3 Results and Discussions 23711.4 Conclusion and Future Work 24012 A Novel Method to Predicting Tumor in Fallopian Tube UsingDenseNet Over Linear Regression with Enhanced Efficiency 243Harish C.M. and Terrance Frederick Fernandez12.1 Introduction 24412.2 Materials and Methods 24612.3 Results and Discussion 25012.4 Conclusion 25713 Protected Medical Images Against Security Threats Using Lasso Regression and K-Means Algorithms 261N. Sainath Reddy and S. Tamilselvan13.1 Introduction 26113.2 Materials and Methods 26213.3 K-Means Classifier 26313.4 Procedure for K-Means Classifier 26313.5 Lasso Regression 26313.6 Procedure for Lasso Regression 26413.7 Statistical Analysis 26413.8 Results 26413.9 Discussion 26613.10 Conclusion 267Part 5: Emerging Trends and Technologies 27114 Predicting the Factors Influencing Alcoholic Consumption of Teenagers Using an Optimized Random Forest Classifier in Comparison with Logistic Regression 273Devineni Giri and M. Gunasekaran14.1 Introduction 27314.2 Materials and Methods 27514.3 Random Forest Classifier 27514.4 Algorithm for Random Forest Classifier 27614.5 Logistic Regression Classifier 27614.6 Algorithm for Logistic Regression Classifier 27614.7 Results 27714.8 Discussion 27914.9 Conclusion 28015 Harnessing Food Waste Potential: Advancing Protein Sequence Motif Analysis with Novel Cluster Sequence Analyzer Machine Learning Model 283U. Vignesh, Geetha S. and Benson Edwin Raj15.1 Introduction 28415.2 Suffix Tree 28915.3 Clustering Algorithms in PPI 29315.4 Classification Agorithms in PPI 29615.5 CSA and PPI Interaction Results 29815.6 Conclusion 30816 "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 311Rana Bandyopadhyay and Aniruddha Banerjee16.1 Introduction 31216.2 Research Background 31316.3 Literature Review 31316.4 Research Gaps 31516.5 Research Methodology 31516.6 Objectives 31516.7 NH Success Story 31516.8 Analysis and Discussion 31616.9 Findings 32116.10 People Analytics and Humane Touch 32516.11 Conclusions 327References 327Index 329