{"product_id":"optimized-predictive-models-in-health-care-using-machine-learning-hardback-9781394174621","title":"Optimized Predictive Models in Health Care Using Machine Learning (Hardback) 9781394174621","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eOptimized Predictive Models in Health Care Using Machine Learning\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\"\u003eSandeep Kumar (Edited by), Anuj Sharma (Edited by), Navneet Kaur (Edited by), Lokesh Pawar (Edited by), Rohit Bajaj (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781394174621, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 19 April 2024\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e384 pages\u003cbr\u003e22.9 x 15.2 x 2.4 cm, 0.844 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\u003cb\u003eOPTIMIZED PREDICTIVE MODELS IN HEALTH CARE USING MACHINE LEARNING\u003c\/b\u003e \u003cp\u003e \u003cb\u003eThis book is a comprehensive guide to developing and implementing optimized predictive models in healthcare using machine learning and is a required resource for researchers, healthcare professionals, and students who wish to know more about real-time applications.\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eThe book focuses on how humans and computers interact to ever-increasing levels of complexity and simplicity and provides content on the theory of optimized predictive model design, evaluation, and user diversity. Predictive modeling, a field of machine learning, has emerged as a powerful tool in healthcare for identifying high-risk patients, predicting disease progression, and optimizing treatment plans. By leveraging data from various sources, predictive models can help healthcare providers make informed decisions, resulting in better patient outcomes and reduced costs.  \u003c\/p\u003e\n\u003cp\u003eOther essential features of the book include: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eprovides detailed guidance on data collection and preprocessing, emphasizing the importance of collecting accurate and reliable data; \u003c\/li\u003e \u003cli\u003eexplains how to transform raw data into meaningful features that can be used to improve the accuracy of predictive models; \u003c\/li\u003e \u003cli\u003egives a detailed overview of machine learning algorithms for predictive modeling in healthcare, discussing the pros and cons of different algorithms and how to choose the best one for a specific application;\u003c\/li\u003e \u003cli\u003eemphasizes validating and evaluating predictive models;\u003c\/li\u003e \u003cli\u003eprovides a comprehensive overview of validation and evaluation techniques and how to evaluate the performance of predictive models using a range of metrics; \u003c\/li\u003e \u003cli\u003ediscusses the challenges and limitations of predictive modeling in healthcare;\u003c\/li\u003e \u003cli\u003ehighlights the ethical and legal considerations that must be considered when developing predictive models and the potential biases that can arise in those models. \u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003cb\u003eAudience\u003c\/b\u003e \u003c\/p\u003e\n\u003cp\u003eThe book will be read by a wide range of professionals who are involved in healthcare, data science, and machine learning.\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\u003e1 Impact of Technology on Daily Food Habits and Their Effects on Health 1\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eNeha Tanwar, Sandeep Kumar and Shilpa Choudhary\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 2\u003c\/p\u003e \u003cp\u003e1.2 Technologies, Foodies, and Consciousness 4\u003c\/p\u003e \u003cp\u003e1.3 Government Programs to Encourage Healthy Choices 7\u003c\/p\u003e \u003cp\u003e1.4 Technology's Impact on Our Food Consumption 7\u003c\/p\u003e \u003cp\u003e1.5 Customized Food is the Future of Food 8\u003c\/p\u003e \u003cp\u003e1.6 Impact of Food Technology and Innovation on Nutrition and Health 9\u003c\/p\u003e \u003cp\u003e1.7 Top Prominent and Emerging Food Technology Trends 10\u003c\/p\u003e \u003cp\u003e1.8 Discussion 18\u003c\/p\u003e \u003cp\u003e1.9 Conclusions 18\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Issues in Healthcare and the Role of Machine Learning in Healthcare 21\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eNidhika Chauhan, Navneet Kaur, Kamaljit Singh Saini and Manjot Kaur\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 22\u003c\/p\u003e \u003cp\u003e2.2 Issues in Healthcare 23\u003c\/p\u003e \u003cp\u003e2.3 Factors Affecting the Health 30\u003c\/p\u003e \u003cp\u003e2.4 Machine Learning in Healthcare 30\u003c\/p\u003e \u003cp\u003e2.5 Conclusion 32\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Improving Accuracy in Predicting Stress Levels of Working Women Using Convolutional Neural Networks 39\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePurude Vaishali Narayanro, Regula Srilakshmi, M. Deepika and P. Lalitha Surya Kumari\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 39\u003c\/p\u003e \u003cp\u003e3.2 Literature Survey 41\u003c\/p\u003e \u003cp\u003e3.3 Proposed Methodology 45\u003c\/p\u003e \u003cp\u003e3.4 Result and Discussion 50\u003c\/p\u003e \u003cp\u003e3.5 Conclusion and Future Scope 54\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Analysis of Smart Technologies in Healthcare 57\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eShikha Jain, Navneet Kaur, Manisha Malhotra and Manjot Kaur\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 57\u003c\/p\u003e \u003cp\u003e4.2 Emerging Technologies in Healthcare 58\u003c\/p\u003e \u003cp\u003e4.3 Literature Review 62\u003c\/p\u003e \u003cp\u003e4.4 Risks and Challenges 65\u003c\/p\u003e \u003cp\u003e4.5 Conclusion 68\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Enhanced Neural Network Ensemble Classification for the Diagnosis of Lung Cancer Disease 73\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eThaventhiran Chandrasekar, Praveen Kumar Karunanithi, K.R. Sekar and Arka Ghosh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 74\u003c\/p\u003e \u003cp\u003e5.2 Algorithm for Classification of Proposed Weight-Optimized Neural Network Ensembles 75\u003c\/p\u003e \u003cp\u003e5.3 Experimental Work and Results 81\u003c\/p\u003e \u003cp\u003e5.4 Conclusion 84\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Feature Selection for Breast Cancer Detection 89\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eKishan Sharda, Mandeep Singh Ramdev, Deepak Rawat and Pawan Bishnoi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 90\u003c\/p\u003e \u003cp\u003e6.2 Literature Review 92\u003c\/p\u003e \u003cp\u003e6.3 Design and Implementation 94\u003c\/p\u003e \u003cp\u003e6.4 Conclusion 100\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 An Optimized Feature-Based Prediction Model for Grouping the Liver Patients 103\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eBhupender Yadav and Rohit Bajaj\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 104\u003c\/p\u003e \u003cp\u003e7.2 Literature Review 106\u003c\/p\u003e \u003cp\u003e7.3 Proposed Methodology 108\u003c\/p\u003e \u003cp\u003e7.4 Results and Discussions 108\u003c\/p\u003e \u003cp\u003e7.5 Conclusion 113\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 A Robust Machine Learning Model for Breast Cancer Prediction 117\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eRachna, Chahil Choudhary and Jatin Thakur\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 118\u003c\/p\u003e \u003cp\u003e8.2 Literature Review 119\u003c\/p\u003e \u003cp\u003e8.3 Proposed Mythology 126\u003c\/p\u003e \u003cp\u003e8.4 Result and Discussion 127\u003c\/p\u003e \u003cp\u003e8.5 Concluding Remarks and Future Scope 132\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Revolutionizing Pneumonia Diagnosis and Prediction Through Deep Neural Networks 135\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eAbhishek Bhola and Monali Gulhane\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 135\u003c\/p\u003e \u003cp\u003e9.2 Literature Work 138\u003c\/p\u003e \u003cp\u003e9.3 Proposed Section 139\u003c\/p\u003e \u003cp\u003e9.4 Result Analysis 142\u003c\/p\u003e \u003cp\u003e9.5 Conclusion and Future Scope 146\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Optimizing Prediction of Liver Disease Using Machine Learning Algorithms 151\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eRachna, Tanish Jain, Deepak Shandilya and Shivangi Gagneja\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 151\u003c\/p\u003e \u003cp\u003e10.2 Related Works 153\u003c\/p\u003e \u003cp\u003e10.3 Proposed Methodology 166\u003c\/p\u003e \u003cp\u003e10.4 Result and Discussions 166\u003c\/p\u003e \u003cp\u003e10.5 Conclusion 170\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Optimized Ensembled Model to Predict Diabetes Using Machine Learning 173\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eKamal, AnujKumar Sharma and Dinesh Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 173\u003c\/p\u003e \u003cp\u003e11.2 Literature Review 175\u003c\/p\u003e \u003cp\u003e11.3 Proposed Methodology 177\u003c\/p\u003e \u003cp\u003e11.4 Results and Discussion 184\u003c\/p\u003e \u003cp\u003e11.5 Concluding Remarks and Future Scope 187\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Wearable Gait Authentication: A Framework for Secure User Identification in Healthcare 195\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eSwathi A., Swathi V., Shilpa Choudhary and Munish Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 195\u003c\/p\u003e \u003cp\u003e12.2 Literature Survey 197\u003c\/p\u003e \u003cp\u003e12.3 Proposed System 199\u003c\/p\u003e \u003cp\u003e12.4 Results and Discussion 203\u003c\/p\u003e \u003cp\u003e12.5 Conclusion and Future Scope 211\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 NLP-Based Speech Analysis Using K-Neighbor Classifier 215\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eRenuka Arora and Rishu Bhatia\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 215\u003c\/p\u003e \u003cp\u003e13.2 Supervised Machine Learning for NLP and Text Analytics 216\u003c\/p\u003e \u003cp\u003e13.3 Unsupervised Machine Learning for NLP and Text Analytics 219\u003c\/p\u003e \u003cp\u003e13.4 Experiments and Results 222\u003c\/p\u003e \u003cp\u003e13.5 Conclusion 225\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Fusion of Various Machine Learning Algorithms for Early Heart Attack Prediction 229\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMonali Gulhane and Sandeep Kumar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 230\u003c\/p\u003e \u003cp\u003e14.2 Literature Review 231\u003c\/p\u003e \u003cp\u003e14.3 Materials and Methods 233\u003c\/p\u003e \u003cp\u003e14.4 Result Analysis 239\u003c\/p\u003e \u003cp\u003e14.5 Conclusion 242\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Machine Learning-Based Approaches for Improving Healthcare Services and Quality of Life (QoL): Opportunities, Issues and Challenges 245\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePankaj Rahi, Rohit Bajaj, Sanjay P. Sood, Monika Dandotiyan and A. Anushya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 246\u003c\/p\u003e \u003cp\u003e15.2 Core Areas of Deep Learning and ML-Modeling in Medical Healthcare 248\u003c\/p\u003e \u003cp\u003e15.3 Use Cases of Machine Learning Modelling in Healthcare Informatics 250\u003c\/p\u003e \u003cp\u003e15.4 Improving the Quality of Services During the Diagnosing and Treatment Processes of Chronicle Diseases 259\u003c\/p\u003e \u003cp\u003e15.5 Limitations and Challenges of ML, DL Modelling in Healthcare Systems 261\u003c\/p\u003e \u003cp\u003e15.6 Conclusion 264\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Developing a Cognitive Learning and Intelligent Data Analysis-Based Framework for Early Disease Detection and Prevention in Younger Adults with Fatigue 273\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eHarish Padmanaban P. C. and Yogesh Kumar Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 274\u003c\/p\u003e \u003cp\u003e16.2 Proposed Framework \"Cognitive-Intelligent Fatigue Detection and Prevention Framework (CIFDPF)\" 275\u003c\/p\u003e \u003cp\u003e16.3 Potential Impact 286\u003c\/p\u003e \u003cp\u003e16.4 Discussion and Limitations 292\u003c\/p\u003e \u003cp\u003e16.5 Future Work 293\u003c\/p\u003e \u003cp\u003e16.6 Conclusion 294\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Machine Learning Approach to Predicting Reliability in Healthcare Using Knowledge Engineering 299\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eKialakun N. Galgal, Kamalakanta Muduli and Ashish Kumar Luhach\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 300\u003c\/p\u003e \u003cp\u003e17.2 Literature Review 302\u003c\/p\u003e \u003cp\u003e17.3 Proposed Methodology 305\u003c\/p\u003e \u003cp\u003e17.4 Implications 310\u003c\/p\u003e \u003cp\u003e17.5 Conclusion 312\u003c\/p\u003e \u003cp\u003e17.6 Limitations and Scope of Future Work 313\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 TPLSTM-Based Deep ANN with Feature Matching Prediction of Lung Cancer 317\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eThaventhiran Chandrasekar, Praveen Kumar Karunanithi, A. Emily Jenifer and Inti Dhiraj\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 318\u003c\/p\u003e \u003cp\u003e18.2 Proposed TP-LSTM-Based Neural Network with Feature Matching for Prediction of Lung Cancer 320\u003c\/p\u003e \u003cp\u003e18.3 Experimental Work and Comparison Analysis 325\u003c\/p\u003e \u003cp\u003e18.4 Conclusion 326\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Analysis of Business Intelligence in Healthcare Using Machine Learning 329\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eVipin Kumar, Chelsi Sen, Arpit Jain, Abhishek Jain and Anu Sharma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 329\u003c\/p\u003e \u003cp\u003e19.2 Data Gathering 331\u003c\/p\u003e \u003cp\u003e19.3 Literature Review 333\u003c\/p\u003e \u003cp\u003e19.4 Research Methodology 334\u003c\/p\u003e \u003cp\u003e19.5 Implementation 335\u003c\/p\u003e \u003cp\u003e19.6 Eligibility Criteria 337\u003c\/p\u003e \u003cp\u003e19.7 Results 337\u003c\/p\u003e \u003cp\u003e19.8 Conclusion and Future Scope 338\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 StressDetect: ML for Mental Stress Prediction 341\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eHimanshu Verma, Nimish Kumar, Yogesh Kumar Sharma and Pankaj Vyas\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 342\u003c\/p\u003e \u003cp\u003e20.2 Related Work 344\u003c\/p\u003e \u003cp\u003e20.3 Materials and Methods 348\u003c\/p\u003e \u003cp\u003e20.4 Results 352\u003c\/p\u003e \u003cp\u003e20.5 Discussion \u0026amp; Conclusions 353\u003c\/p\u003e \u003cp\u003eReferences 355\u003c\/p\u003e \u003cp\u003eIndex 359\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Computer science [\u003ca title=\"See our other books on Computer science\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Computer%20science%20%5BUY%5D%22\"\u003eUY\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":52431056109848,"sku":"9781394174621","price":114.59,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781394174621.jpg?v=1784770152","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/optimized-predictive-models-in-health-care-using-machine-learning-hardback-9781394174621","provider":"Freshly Printed Books","version":"1.0","type":"link"}