Optimized Predictive Models in Health Care Using Machine Learning
Description
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.
More details
Other editions
Additional editions
Persons
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.
Content
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
