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240503 ||| eng |
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|a 9781394175376
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| 050 |
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|a RA409.5
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| 100 |
1 |
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|a Kumar, Sandeep
|e editor
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| 245 |
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|a Optimized predictive models in health care using machine learning
|c edited by Sandeep Kumar, Anuj Sharma, Navneet Kaur, Lokesh Pawar and Rohit Bajaj
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| 260 |
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|a Hoboken, NJ
|b Wiley
|c 2024
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| 300 |
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|a 384 pages
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| 505 |
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|a Preface -- 1 Impact of Technology on Daily Food Habits and Their Effects on Health 1 Neha Tanwar, Sandeep Kumar and Shilpa Choudhary -- 1.1 Introduction -- 1.2 Technologies, Foodies, and Consciousness -- 1.3 Government Programs to Encourage Healthy Choices -- 1.4 Technology's Impact on Our Food Consumption -- 1.5 Customized Food is the Future of Food -- 1.6 Impact of Food Technology and Innovation on Nutrition and Health -- 1.7 Top Prominent and Emerging Food Technology Trends -- 1.8 Discussion -- 1.9 Conclusions -- 2 Issues in Healthcare and the Role of Machine Learning in Healthcare 21 Nidhika Chauhan, Navneet Kaur, Kamaljit Singh Saini and Manjot Kaur -- 2.1 Introduction -- 2.2 Issues in Healthcare -- 2.3 Factors Affecting the Health -- 2.4 Machine Learning in Healthcare -- 2.5 Conclusion --
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|a Includes bibliographical references and index
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|a 18.2 Proposed TP-LSTM-Based Neural Network with Feature Matching for Prediction of Lung Cancer -- 18.3 Experimental Work and Comparison Analysis -- 18.4 Conclusion -- 19 Analysis of Business Intelligence in Healthcare Using Machine Learning 329 Vipin Kumar, Chelsi Sen, Arpit Jain, Abhishek Jain and Anu Sharma -- 19.1 Introduction -- 19.2 Data Gathering -- 19.3 Literature Review -- 19.4 Research Methodology -- 19.5 Implementation -- 19.6 Eligibility Criteria -- 19.7 Results -- 19.8 Conclusion and Future Scope -- 20 StressDetect: ML for Mental Stress Prediction 341 Himanshu Verma, Nimish Kumar, Yogesh Kumar Sharma and Pankaj Vyas -- 20.1 Introduction -- 20.2 Related Work -- 20.3 Materials and Methods -- 20.4 Results -- 20.5 Discussion & Conclusions -- References -- Index
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|a 3 Improving Accuracy in Predicting Stress Levels of Working Women Using Convolutional Neural Networks 39 Purude Vaishali Narayanro, Regula Srilakshmi, M. Deepika and P. Lalitha Surya Kumari -- 3.1 Introduction -- 3.2 Literature Survey -- 3.3 Proposed Methodology -- 3.4 Result and Discussion -- 3.5 Conclusion and Future Scope -- 4 Analysis of Smart Technologies in Healthcare 57 Shikha Jain, Navneet Kaur, Manisha Malhotra and Manjot Kaur -- 4.1 Introduction -- 4.2 Emerging Technologies in Healthcare -- 4.3 Literature Review -- 4.4 Risks and Challenges -- 4.5 Conclusion -- 5 Enhanced Neural Network Ensemble Classification for the Diagnosis of Lung Cancer Disease 73 Thaventhiran Chandrasekar, Praveen Kumar Karunanithi, K.R. Sekar and Arka Ghosh -- 5.1 Introduction -- 5.2 Algorithm for Classification of Proposed Weight-Optimized Neural Network Ensembles -- 5.3 Experimental Work and Results -- 5.4 Conclusion --
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|a 15.6 Conclusion -- 16 Developing a Cognitive Learning and Intelligent Data Analysis-Based Framework for Early Disease Detection and Prevention in Younger Adults with Fatigue 273 Harish Padmanaban P. C. and Yogesh Kumar Sharma -- 16.1 Introduction -- 16.2 Proposed Framework "Cognitive-Intelligent Fatigue Detection and Prevention Framework (CIFDPF)" -- 16.3 Potential Impact -- 16.4 Discussion and Limitations -- 16.5 Future Work -- 16.6 Conclusion -- 17 Machine Learning Approach to Predicting Reliability in Healthcare Using Knowledge Engineering 299 Kialakun N. Galgal, Kamalakanta Muduli and Ashish Kumar Luhach -- 17.1 Introduction -- 17.2 Literature Review -- 17.3 Proposed Methodology -- 17.4 Implications -- 17.5 Conclusion -- 17.6 Limitations and Scope of Future Work -- 18 TPLSTM-Based Deep ANN with Feature Matching Prediction of Lung Cancer 317 Thaventhiran Chandrasekar, Praveen Kumar Karunanithi, A. Emily Jenifer and Inti Dhiraj -- 18.1 Introduction --
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|a 10 Optimizing Prediction of Liver Disease Using Machine Learning Algorithms 151 Rachna, Tanish Jain, Deepak Shandilya and Shivangi Gagneja -- 10.1 Introduction -- 10.2 Related Works -- 10.3 Proposed Methodology -- 10.4 Result and Discussions -- 10.5 Conclusion -- 11 Optimized Ensembled Model to Predict Diabetes Using Machine Learning 173 Kamal, AnujKumar Sharma and Dinesh Kumar -- 11.1 Introduction -- 11.2 Literature Review -- 11.3 Proposed Methodology -- 11.4 Results and Discussion -- 11.5 Concluding Remarks and Future Scope -- 12 Wearable Gait Authentication: A Framework for Secure User Identification in Healthcare 195 Swathi A., Swathi V., Shilpa Choudhary and Munish Kumar -- 12.1 Introduction -- 12.2 Literature Survey -- 12.3 Proposed System -- 12.4 Results and Discussion -- 12.5 Conclusion and Future Scope -- 13 NLP-Based Speech Analysis Using K-Neighbor Classifier 215 Renuka Arora and Rishu Bhatia -- 13.1 Introduction --
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|a 13.2 Supervised Machine Learning for NLP and Text Analytics -- 13.3 Unsupervised Machine Learning for NLP and Text Analytics -- 13.4 Experiments and Results -- 13.5 Conclusion -- 14 Fusion of Various Machine Learning Algorithms for Early Heart Attack Prediction 229 Monali Gulhane and Sandeep Kumar -- 14.1 Introduction -- 14.2 Literature Review -- 14.3 Materials and Methods -- 14.4 Result Analysis -- 14.5 Conclusion -- 15 Machine Learning-Based Approaches for Improving Healthcare Services and Quality of Life (QoL): Opportunities, Issues and Challenges 245 Pankaj Rahi, Rohit Bajaj, Sanjay P. Sood, Monika Dandotiyan and A. Anushya -- 15.1 Introduction -- 15.2 Core Areas of Deep Learning and ML-Modeling in Medical Healthcare -- 15.3 Use Cases of Machine Learning Modelling in Healthcare Informatics -- 15.4 Improving the Quality of Services During the Diagnosing and Treatment Processes of Chronicle Diseases -- 15.5 Limitations and Challenges of ML, DL Modelling in Healthcare Systems --
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|a 6 Feature Selection for Breast Cancer Detection 89 Kishan Sharda, Mandeep Singh Ramdev, Deepak Rawat and Pawan Bishnoi -- 6.1 Introduction -- 6.2 Literature Review -- 6.3 Design and Implementation -- 6.4 Conclusion -- 7 An Optimized Feature-Based Prediction Model for Grouping the Liver Patients 103 Bhupender Yadav and Rohit Bajaj -- 7.1 Introduction -- 7.2 Literature Review -- 7.3 Proposed Methodology -- 7.4 Results and Discussions -- 7.5 Conclusion -- 8 A Robust Machine Learning Model for Breast Cancer Prediction 117 Rachna, Chahil Choudhary and Jatin Thakur -- 8.1 Introduction -- 8.2 Literature Review -- 8.3 Proposed Mythology -- 8.4 Result and Discussion -- 8.5 Concluding Remarks and Future Scope -- 9 Revolutionizing Pneumonia Diagnosis and Prediction Through Deep Neural Networks 135 Abhishek Bhola and Monali Gulhane -- 9.1 Introduction -- 9.2 Literature Work -- 9.3 Proposed Section -- 9.4 Result Analysis -- 9.5 Conclusion and Future Scope --
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| 653 |
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|a Intelligence artificielle en médecine
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| 653 |
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|a Machine learning / http://id.loc.gov/authorities/subjects/sh85079324
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| 653 |
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|a Medical technology / http://id.loc.gov/authorities/subjects/sh85083046
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| 653 |
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|a Artificial intelligence / Medical applications / http://id.loc.gov/authorities/subjects/sh88003000
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| 653 |
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|a Apprentissage automatique
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| 653 |
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|a Medical statistics / http://id.loc.gov/authorities/subjects/sh85083031
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| 653 |
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|a Medical Laboratory Science
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| 653 |
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|a Machine Learning
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| 653 |
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|a Technologie médicale
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| 700 |
1 |
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|a Sharma, Anuj
|e editor
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| 700 |
1 |
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|a Kaur, Navneet
|e editor
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| 700 |
1 |
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|a Pawar, Lokesh
|e editor
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 024 |
8 |
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|a 10.1002/9781394175376
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| 776 |
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|z 139417537X
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| 776 |
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|z 9781394175376
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| 776 |
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|z 9781394174621
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| 856 |
4 |
0 |
|u https://learning.oreilly.com/library/view/~/9781394174621/?ar
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 610.2/1
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| 082 |
0 |
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|a 500
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| 082 |
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|a 519.5
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| 520 |
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|a Audience The book will be read by a wide range of professionals who are involved in healthcare, data science, and machine learning
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| 520 |
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|a Other essential features of the book include: provides detailed guidance on data collection and preprocessing, emphasizing the importance of collecting accurate and reliable data; explains how to transform raw data into meaningful features that can be used to improve the accuracy of predictive models; gives 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; emphasizes validating and evaluating predictive models; provides a comprehensive overview of validation and evaluation techniques and how to evaluate the performance of predictive models using a range of metrics; discusses the challenges and limitations of predictive modeling in healthcare; highlights the ethical and legal considerations that must be considered when developing predictive models and the potential biases that can arise in those models.
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| 520 |
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|a OPTIMIZED PREDICTIVE MODELS IN HEALTH CARE USING MACHINE LEARNING This 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. The 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.
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