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…mehr
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. 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. Audience The book will be read by a wide range of professionals who are involved in healthcare, data science, and machine learning.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Sandeep Kumar, PhD, is a professor in the Department of Computer Science and Engineering, K L Deemed to be University, Vijayawada, Andhra Pradesh, India. He has been granted six patents and successfully filed another ten. He has published more than 100 research papers in various national and international journals and proceedings of reputed national and international conferences. Anuj Sharma, PhD, is a professor at Maharshi Dayanand University, Rohtak, India. He has 19 years of teaching and administrative experience and has published more than 50 journal articles. Navneet Kaur, PhD, is a professor in the Department of Computer Science & Engineering, Chandigarh University, India. She is the awardee of the Best Engineering College Teacher Award for Punjab State for the year 2019 and has published more than 35 research articles in reputed SCI journals and conferences. Lokesh Pawar, PhD, is an assistant professor at Chandigarh University, India. He has filed two patents and has published multiple research articles in many SCI journals. Rohit Bajaj, PhD, is an associate professor in the Department of Computer Science & Engineering, Chandigarh University, India. He has 12 years of teaching research experience and has published 60 papers in refereed journals and conferences.
Inhaltsangabe
Preface xv 1 Impact of Technology on Daily Food Habits and Their Effects on Health 1 Neha Tanwar, Sandeep Kumar and Shilpa Choudhary 1.1 Introduction 2 1.2 Technologies, Foodies, and Consciousness 4 1.3 Government Programs to Encourage Healthy Choices 7 1.4 Technology's Impact on Our Food Consumption 7 1.5 Customized Food is the Future of Food 8 1.6 Impact of Food Technology and Innovation on Nutrition and Health 9 1.7 Top Prominent and Emerging Food Technology Trends 10 1.8 Discussion 18 1.9 Conclusions 18 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 22 2.2 Issues in Healthcare 23 2.3 Factors Affecting the Health 30 2.4 Machine Learning in Healthcare 30 2.5 Conclusion 32 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 39 3.2 Literature Survey 41 3.3 Proposed Methodology 45 3.4 Result and Discussion 50 3.5 Conclusion and Future Scope 54 4 Analysis of Smart Technologies in Healthcare 57 Shikha Jain, Navneet Kaur, Manisha Malhotra and Manjot Kaur 4.1 Introduction 57 4.2 Emerging Technologies in Healthcare 58 4.3 Literature Review 62 4.4 Risks and Challenges 65 4.5 Conclusion 68 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 74 5.2 Algorithm for Classification of Proposed Weight-Optimized Neural Network Ensembles 75 5.3 Experimental Work and Results 81 5.4 Conclusion 84 6 Feature Selection for Breast Cancer Detection 89 Kishan Sharda, Mandeep Singh Ramdev, Deepak Rawat and Pawan Bishnoi 6.1 Introduction 90 6.2 Literature Review 92 6.3 Design and Implementation 94 6.4 Conclusion 100 7 An Optimized Feature-Based Prediction Model for Grouping the Liver Patients 103 Bhupender Yadav and Rohit Bajaj 7.1 Introduction 104 7.2 Literature Review 106 7.3 Proposed Methodology 108 7.4 Results and Discussions 108 7.5 Conclusion 113 8 A Robust Machine Learning Model for Breast Cancer Prediction 117 Rachna, Chahil Choudhary and Jatin Thakur 8.1 Introduction 118 8.2 Literature Review 119 8.3 Proposed Mythology 126 8.4 Result and Discussion 127 8.5 Concluding Remarks and Future Scope 132 9 Revolutionizing Pneumonia Diagnosis and Prediction Through Deep Neural Networks 135 Abhishek Bhola and Monali Gulhane 9.1 Introduction 135 9.2 Literature Work 138 9.3 Proposed Section 139 9.4 Result Analysis 142 9.5 Conclusion and Future Scope 146 10 Optimizing Prediction of Liver Disease Using Machine Learning Algorithms 151 Rachna, Tanish Jain, Deepak Shandilya and Shivangi Gagneja 10.1 Introduction 151 10.2 Related Works 153 10.3 Proposed Methodology 166 10.4 Result and Discussions 166 10.5 Conclusion 170 11 Optimized Ensembled Model to Predict Diabetes Using Machine Learning 173 Kamal, AnujKumar Sharma and Dinesh Kumar 11.1 Introduction 173 11.2 Literature Review 175 11.3 Proposed Methodology 177 11.4 Results and Discussion 184 11.5 Concluding Remarks and Future Scope 187 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 195 12.2 Literature Survey 197 12.3 Proposed System 199 12.4 Results and Discussion 203 12.5 Conclusion and Future Scope 211 13 NLP-Based Speech Analysis Using K-Neighbor Classifier 215 Renuka Arora and Rishu Bhatia 13.1 Introduction 215 13.2 Supervised Machine Learning for NLP and Text Analytics 216 13.3 Unsupervised Machine Learning for NLP and Text Analytics 219 13.4 Experiments and Results 222 13.5 Conclusion 225 14 Fusion of Various Machine Learning Algorithms for Early Heart Attack Prediction 229 Monali Gulhane and Sandeep Kumar 14.1 Introduction 230 14.2 Literature Review 231 14.3 Materials and Methods 233 14.4 Result Analysis 239 14.5 Conclusion 242 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 246 15.2 Core Areas of Deep Learning and ML-Modeling in Medical Healthcare 248 15.3 Use Cases of Machine Learning Modelling in Healthcare Informatics 250 15.4 Improving the Quality of Services During the Diagnosing and Treatment Processes of Chronicle Diseases 259 15.5 Limitations and Challenges of ML, DL Modelling in Healthcare Systems 261 15.6 Conclusion 264 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 274 16.2 Proposed Framework "Cognitive-Intelligent Fatigue Detection and Prevention Framework (CIFDPF)" 275 16.3 Potential Impact 286 16.4 Discussion and Limitations 292 16.5 Future Work 293 16.6 Conclusion 294 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 300 17.2 Literature Review 302 17.3 Proposed Methodology 305 17.4 Implications 310 17.5 Conclusion 312 17.6 Limitations and Scope of Future Work 313 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 318 18.2 Proposed TP-LSTM-Based Neural Network with Feature Matching for Prediction of Lung Cancer 320 18.3 Experimental Work and Comparison Analysis 325 18.4 Conclusion 326 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 329 19.2 Data Gathering 331 19.3 Literature Review 333 19.4 Research Methodology 334 19.5 Implementation 335 19.6 Eligibility Criteria 337 19.7 Results 337 19.8 Conclusion and Future Scope 338 20 StressDetect: ML for Mental Stress Prediction 341 Himanshu Verma, Nimish Kumar, Yogesh Kumar Sharma and Pankaj Vyas 20.1 Introduction 342 20.2 Related Work 344 20.3 Materials and Methods 348 20.4 Results 352 20.5 Discussion & Conclusions 353 References 355 Index 359
Preface xv 1 Impact of Technology on Daily Food Habits and Their Effects on Health 1 Neha Tanwar, Sandeep Kumar and Shilpa Choudhary 1.1 Introduction 2 1.2 Technologies, Foodies, and Consciousness 4 1.3 Government Programs to Encourage Healthy Choices 7 1.4 Technology's Impact on Our Food Consumption 7 1.5 Customized Food is the Future of Food 8 1.6 Impact of Food Technology and Innovation on Nutrition and Health 9 1.7 Top Prominent and Emerging Food Technology Trends 10 1.8 Discussion 18 1.9 Conclusions 18 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 22 2.2 Issues in Healthcare 23 2.3 Factors Affecting the Health 30 2.4 Machine Learning in Healthcare 30 2.5 Conclusion 32 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 39 3.2 Literature Survey 41 3.3 Proposed Methodology 45 3.4 Result and Discussion 50 3.5 Conclusion and Future Scope 54 4 Analysis of Smart Technologies in Healthcare 57 Shikha Jain, Navneet Kaur, Manisha Malhotra and Manjot Kaur 4.1 Introduction 57 4.2 Emerging Technologies in Healthcare 58 4.3 Literature Review 62 4.4 Risks and Challenges 65 4.5 Conclusion 68 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 74 5.2 Algorithm for Classification of Proposed Weight-Optimized Neural Network Ensembles 75 5.3 Experimental Work and Results 81 5.4 Conclusion 84 6 Feature Selection for Breast Cancer Detection 89 Kishan Sharda, Mandeep Singh Ramdev, Deepak Rawat and Pawan Bishnoi 6.1 Introduction 90 6.2 Literature Review 92 6.3 Design and Implementation 94 6.4 Conclusion 100 7 An Optimized Feature-Based Prediction Model for Grouping the Liver Patients 103 Bhupender Yadav and Rohit Bajaj 7.1 Introduction 104 7.2 Literature Review 106 7.3 Proposed Methodology 108 7.4 Results and Discussions 108 7.5 Conclusion 113 8 A Robust Machine Learning Model for Breast Cancer Prediction 117 Rachna, Chahil Choudhary and Jatin Thakur 8.1 Introduction 118 8.2 Literature Review 119 8.3 Proposed Mythology 126 8.4 Result and Discussion 127 8.5 Concluding Remarks and Future Scope 132 9 Revolutionizing Pneumonia Diagnosis and Prediction Through Deep Neural Networks 135 Abhishek Bhola and Monali Gulhane 9.1 Introduction 135 9.2 Literature Work 138 9.3 Proposed Section 139 9.4 Result Analysis 142 9.5 Conclusion and Future Scope 146 10 Optimizing Prediction of Liver Disease Using Machine Learning Algorithms 151 Rachna, Tanish Jain, Deepak Shandilya and Shivangi Gagneja 10.1 Introduction 151 10.2 Related Works 153 10.3 Proposed Methodology 166 10.4 Result and Discussions 166 10.5 Conclusion 170 11 Optimized Ensembled Model to Predict Diabetes Using Machine Learning 173 Kamal, AnujKumar Sharma and Dinesh Kumar 11.1 Introduction 173 11.2 Literature Review 175 11.3 Proposed Methodology 177 11.4 Results and Discussion 184 11.5 Concluding Remarks and Future Scope 187 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 195 12.2 Literature Survey 197 12.3 Proposed System 199 12.4 Results and Discussion 203 12.5 Conclusion and Future Scope 211 13 NLP-Based Speech Analysis Using K-Neighbor Classifier 215 Renuka Arora and Rishu Bhatia 13.1 Introduction 215 13.2 Supervised Machine Learning for NLP and Text Analytics 216 13.3 Unsupervised Machine Learning for NLP and Text Analytics 219 13.4 Experiments and Results 222 13.5 Conclusion 225 14 Fusion of Various Machine Learning Algorithms for Early Heart Attack Prediction 229 Monali Gulhane and Sandeep Kumar 14.1 Introduction 230 14.2 Literature Review 231 14.3 Materials and Methods 233 14.4 Result Analysis 239 14.5 Conclusion 242 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 246 15.2 Core Areas of Deep Learning and ML-Modeling in Medical Healthcare 248 15.3 Use Cases of Machine Learning Modelling in Healthcare Informatics 250 15.4 Improving the Quality of Services During the Diagnosing and Treatment Processes of Chronicle Diseases 259 15.5 Limitations and Challenges of ML, DL Modelling in Healthcare Systems 261 15.6 Conclusion 264 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 274 16.2 Proposed Framework "Cognitive-Intelligent Fatigue Detection and Prevention Framework (CIFDPF)" 275 16.3 Potential Impact 286 16.4 Discussion and Limitations 292 16.5 Future Work 293 16.6 Conclusion 294 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 300 17.2 Literature Review 302 17.3 Proposed Methodology 305 17.4 Implications 310 17.5 Conclusion 312 17.6 Limitations and Scope of Future Work 313 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 318 18.2 Proposed TP-LSTM-Based Neural Network with Feature Matching for Prediction of Lung Cancer 320 18.3 Experimental Work and Comparison Analysis 325 18.4 Conclusion 326 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 329 19.2 Data Gathering 331 19.3 Literature Review 333 19.4 Research Methodology 334 19.5 Implementation 335 19.6 Eligibility Criteria 337 19.7 Results 337 19.8 Conclusion and Future Scope 338 20 StressDetect: ML for Mental Stress Prediction 341 Himanshu Verma, Nimish Kumar, Yogesh Kumar Sharma and Pankaj Vyas 20.1 Introduction 342 20.2 Related Work 344 20.3 Materials and Methods 348 20.4 Results 352 20.5 Discussion & Conclusions 353 References 355 Index 359
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