{"product_id":"medical-analytics-for-clinical-and-healthcare-applications-hardback-9781394301454","title":"Medical Analytics for Clinical and Healthcare Applications (Hardback) 9781394301454","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eMedical Analytics for Clinical and Healthcare Applications\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eKanak Kalita (Edited by), Kalita (Author), Divya Zindani (Edited by), Narayanan Ganesh (Edited by), Xiao-Zhi Gao (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394301454, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 5 September 2025\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e352 pages\u003cbr\u003e28 x 19 x 2.5 cm, 0.726 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eThe 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.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eIn today’s rapidly advancing healthcare landscape, the integration of medical analytics has become essential for improving patient outcomes, clinical efficiency, and decision-making. \u003ci\u003eMedical Analytics for Clinical and Healthcare Applications\u003c\/i\u003e 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. \u003c\/p\u003e\n\u003cp\u003eSpanning 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. \u003ci\u003eMedical Analytics for Clinical and Healthcare Applications\u003c\/i\u003e 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. \u003c\/p\u003e\n\u003cp\u003eReaders will find the volume: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eExplores the latest medical analytics techniques applied across clinical settings, from diagnosis to treatment optimization;\u003c\/li\u003e \u003cli\u003eFeatures real-world case studies and tools for implementing data-driven solutions in healthcare;\u003c\/li\u003e \u003cli\u003eBridges the gap between healthcare professionals, data scientists, and engineers for collaborative innovation in medical technologies;\u003c\/li\u003e \u003cli\u003eProvides foresight into emerging trends and technologies shaping the future of healthcare analytics.\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003cb\u003eAudience\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eHealthcare professionals, clinical researchers, medical data scientists, biomedical engineers, IT professionals, academics, and policymakers focused on the intersection of medicine and data analytics.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1: Foundations of Medical Analytics 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Exploring Trends in Depression and Anxiety Using Machine and Deep Learning Models 3\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eGarvit Jakar, Timothy George, Parvathi R., Pattabiraman V. and Xiaohui Yuan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 4\u003c\/p\u003e \u003cp\u003e1.2 Exploratory Data Analysis 6\u003c\/p\u003e \u003cp\u003e1.3 Problem Statement and Motivation 7\u003c\/p\u003e \u003cp\u003e1.4 Literature Survey 8\u003c\/p\u003e \u003cp\u003e1.5 Data Visualization 9\u003c\/p\u003e \u003cp\u003e1.6 Overview of Dataset 10\u003c\/p\u003e \u003cp\u003e1.7 Methodology 13\u003c\/p\u003e \u003cp\u003e1.8 Modules 15\u003c\/p\u003e \u003cp\u003e1.9 Results and Discussion 26\u003c\/p\u003e \u003cp\u003e1.10 Conclusion 28\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2: Disease Detection and Diagnosis 31\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 An Innovative Framework for the Detection and Classification of Breast Cancer Disease Using Logistic Regression Compared with Back Propagation Neural Network 33\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eK. Reema Sekhar and Ashley Thomas\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 34\u003c\/p\u003e \u003cp\u003e2.2 Materials and Methods 36\u003c\/p\u003e \u003cp\u003e2.3 Results 39\u003c\/p\u003e \u003cp\u003e2.4 Discussion 42\u003c\/p\u003e \u003cp\u003e2.5 Conclusion 45\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 An Approach to Conduct the Diabetes Prediction Using AdaBoost Algorithm Compared with Decision Tree Classifier Algorithm 49\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eP. Jaswanth Reddy and R. Thalapathi Rajasekaran\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 50\u003c\/p\u003e \u003cp\u003e3.2 Materials and Methods 53\u003c\/p\u003e \u003cp\u003e3.3 Results and Discussion 55\u003c\/p\u003e \u003cp\u003e3.4 Conclusion 61\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Efficient Net V2-Based Pneumonia Detection: A Comparative Study with Transfer Learning Models 65\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSuguna M., Shane V. Jose, Om Kumar C.U., Gunasekaran T. and Prakash D.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 66\u003c\/p\u003e \u003cp\u003e4.2 Related Works 67\u003c\/p\u003e \u003cp\u003e4.3 Materials and Methods 71\u003c\/p\u003e \u003cp\u003e4.4 Results and Discussion 79\u003c\/p\u003e \u003cp\u003e4.5 Conclusion and Future Work 90\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 A Histogram Equalized Median Filtered SIFT–EfficientNet Based on Deep Learning Approach for Lung Disease Detection 93\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSuguna M., Pujala Shree Lekha, Om Kumar C.U., Arunmozhi M. and Prakash D.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 94\u003c\/p\u003e \u003cp\u003e5.2 Related Works 96\u003c\/p\u003e \u003cp\u003e5.3 Materials and Methods 98\u003c\/p\u003e \u003cp\u003e5.4 Performance Measure 112\u003c\/p\u003e \u003cp\u003e5.5 Results and Discussion 113\u003c\/p\u003e \u003cp\u003e5.6 Conclusion and Future Work 119\u003c\/p\u003e \u003cp\u003ePart 3: Predictive Analytics in Healthcare 125\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Comparing the Efficiency of ResNet-50 and Convolutional Neural Networks for Facial Mask Detection 127\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eShaik Khaleel Basha and K. Nattar Kannan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 128\u003c\/p\u003e \u003cp\u003e6.2 Materials and Methods 131\u003c\/p\u003e \u003cp\u003e6.3 ResNet-50 Architecture 132\u003c\/p\u003e \u003cp\u003e6.4 Convolutional Neural Networks (CNN) 133\u003c\/p\u003e \u003cp\u003e6.5 Statistical Analysis 134\u003c\/p\u003e \u003cp\u003e6.6 Results and Discussion 135\u003c\/p\u003e \u003cp\u003e6.7 Conclusion 142\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Enhancing Accuracy in Predicting Knee Osteoarthritis Progression Using Kellgren–Lawrence Grade Compared with Deep Convolutional Neural Network 145\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSai Srinivasa and Malarkodi K.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 146\u003c\/p\u003e \u003cp\u003e7.2 Materials and Methods 149\u003c\/p\u003e \u003cp\u003e7.3 Results and Discussion 153\u003c\/p\u003e \u003cp\u003e7.4 Conclusion 158\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 A Comparative Analysis of Support Vector Machine over K-Neighbors Classifier for Predicting Hospital Mortality with Improved Accuracy 161\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePrabhu Kumar Adi and C. Anitha\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 162\u003c\/p\u003e \u003cp\u003e8.2 Materials and Methods 166\u003c\/p\u003e \u003cp\u003e8.3 Results and Discussion 170\u003c\/p\u003e \u003cp\u003e8.4 Conclusion 175\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Asthma Prediction Using Vowel Inspiration: A Machine Learning Approach 179\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSandhya Prasad, Anik Bhaumik, Suvidha Rupesh Kumar, Rama Parvathy L., Heshalini Rajagopal and Janani S.\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 180\u003c\/p\u003e \u003cp\u003e9.2 Literature Survey 182\u003c\/p\u003e \u003cp\u003e9.3 Motivation and Background 185\u003c\/p\u003e \u003cp\u003e9.4 Proposed Method 186\u003c\/p\u003e \u003cp\u003e9.5 Discussion 194\u003c\/p\u003e \u003cp\u003e9.6 Results 200\u003c\/p\u003e \u003cp\u003e9.7 Conclusion 202\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 4: Medical Data Analysis and Security 207\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Improvement of Accuracy in Prevention of Medical Images from Security Threats Using Novel Lasso Regression in Comparison with K-Means Classifier 209\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eK. Raghul and M. Kalaiyarasi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 210\u003c\/p\u003e \u003cp\u003e10.2 Materials and Methods 213\u003c\/p\u003e \u003cp\u003e10.3 Result 216\u003c\/p\u003e \u003cp\u003e10.4 Discussion 220\u003c\/p\u003e \u003cp\u003e10.5 Conclusion 221\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Renal Cancer Detection from Histopathological Images Using Deep Learning 225\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eAkhil Kumar, R. Krithiga, S. Suseela, B. Swarna and T. Karthikeyan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 226\u003c\/p\u003e \u003cp\u003e11.2 Materials and Methods 229\u003c\/p\u003e \u003cp\u003e11.3 Results and Discussions 237\u003c\/p\u003e \u003cp\u003e11.4 Conclusion and Future Work 240\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 A Novel Method to Predicting Tumor in Fallopian Tube Using\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDenseNet Over Linear Regression with Enhanced Efficiency 243\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eHarish C.M. and Terrance Frederick Fernandez\u003c\/p\u003e \u003cp\u003e12.1 Introduction 244\u003c\/p\u003e \u003cp\u003e12.2 Materials and Methods 246\u003c\/p\u003e \u003cp\u003e12.3 Results and Discussion 250\u003c\/p\u003e \u003cp\u003e12.4 Conclusion 257\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Protected Medical Images Against Security Threats Using Lasso Regression and K-Means Algorithms 261\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eN. Sainath Reddy and S. Tamilselvan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 261\u003c\/p\u003e \u003cp\u003e13.2 Materials and Methods 262\u003c\/p\u003e \u003cp\u003e13.3 K-Means Classifier 263\u003c\/p\u003e \u003cp\u003e13.4 Procedure for K-Means Classifier 263\u003c\/p\u003e \u003cp\u003e13.5 Lasso Regression 263\u003c\/p\u003e \u003cp\u003e13.6 Procedure for Lasso Regression 264\u003c\/p\u003e \u003cp\u003e13.7 Statistical Analysis 264\u003c\/p\u003e \u003cp\u003e13.8 Results 264\u003c\/p\u003e \u003cp\u003e13.9 Discussion 266\u003c\/p\u003e \u003cp\u003e13.10 Conclusion 267\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 5: Emerging Trends and Technologies 271\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Predicting the Factors Influencing Alcoholic Consumption of Teenagers Using an Optimized Random Forest Classifier in Comparison with Logistic Regression 273\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDevineni Giri and M. Gunasekaran\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 273\u003c\/p\u003e \u003cp\u003e14.2 Materials and Methods 275\u003c\/p\u003e \u003cp\u003e14.3 Random Forest Classifier 275\u003c\/p\u003e \u003cp\u003e14.4 Algorithm for Random Forest Classifier 276\u003c\/p\u003e \u003cp\u003e14.5 Logistic Regression Classifier 276\u003c\/p\u003e \u003cp\u003e14.6 Algorithm for Logistic Regression Classifier 276\u003c\/p\u003e \u003cp\u003e14.7 Results 277\u003c\/p\u003e \u003cp\u003e14.8 Discussion 279\u003c\/p\u003e \u003cp\u003e14.9 Conclusion 280\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Harnessing Food Waste Potential: Advancing Protein Sequence Motif Analysis with Novel Cluster Sequence Analyzer Machine Learning Model 283\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eU. Vignesh, Geetha S. and Benson Edwin Raj\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 284\u003c\/p\u003e \u003cp\u003e15.2 Suffix Tree 289\u003c\/p\u003e \u003cp\u003e15.3 Clustering Algorithms in PPI 293\u003c\/p\u003e \u003cp\u003e15.4 Classification Agorithms in PPI 296\u003c\/p\u003e \u003cp\u003e15.5 CSA and PPI Interaction Results 298\u003c\/p\u003e \u003cp\u003e15.6 Conclusion 308\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 \"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\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eRana Bandyopadhyay and Aniruddha Banerjee\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 312\u003c\/p\u003e \u003cp\u003e16.2 Research Background 313\u003c\/p\u003e \u003cp\u003e16.3 Literature Review 313\u003c\/p\u003e \u003cp\u003e16.4 Research Gaps 315\u003c\/p\u003e \u003cp\u003e16.5 Research Methodology 315\u003c\/p\u003e \u003cp\u003e16.6 Objectives 315\u003c\/p\u003e \u003cp\u003e16.7 NH Success Story 315\u003c\/p\u003e \u003cp\u003e16.8 Analysis and Discussion 316\u003c\/p\u003e \u003cp\u003e16.9 Findings 321\u003c\/p\u003e \u003cp\u003e16.10 People Analytics and Humane Touch 325\u003c\/p\u003e \u003cp\u003e16.11 Conclusions 327\u003c\/p\u003e \u003cp\u003eReferences 327\u003cbr\u003eIndex 329\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Other branches of medicine [\u003ca title=\"See our other books on Other branches of medicine\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Other%20branches%20of%20medicine%20%5BMM%5D%22\"\u003eMM\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-Scrivener","offers":[{"title":"Brand New","offer_id":52433516921112,"sku":"9781394301454","price":122.29,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394301454.jpg?v=1784853424","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/medical-analytics-for-clinical-and-healthcare-applications-hardback-9781394301454","provider":"Freshly Printed Books","version":"1.0","type":"link"}